Eco-efficiency monitoring and exploration platform for semiconductor manufacturing

Abstract

Techniques directed to an eco-efficiency monitoring and probing platform for semiconductor manufacturing. One method includes receiving, by a processing device, first data indicative of updates to a substrate manufacturing system having a first configuration of manufacturing equipment and operating according to one or more process procedures. The method further includes determining, by the processing device, environmental resource data using the first data in conjunction with a digital replica. The digital replica includes a digital replica of the substrate manufacturing system. The environmental resource usage data is indicative of environmental resource consumption corresponding to performing the one or more process procedures by the substrate manufacturing system incorporating the updates. The method further includes providing, by the processing device, the environmental resource usage data for display on a graphical user interface (GUI).

Description

[Technical Field]

[0001] This specification relates generally to the environmental impact of semiconductor manufacturing facilities. More particularly, this specification relates to monitoring the ecological efficiency / eco-efficiency of, identifying modifications to, and implementing optimization of semiconductor manufacturing processes and semiconductor manufacturing facilities that perform functions related to the manufacturing processes. [Background technology]

[0002] The continuing demand for electronic devices demands an ever-greater demand for semiconductor wafers. The increased manufacturing to produce these wafers places a significant burden on the environment in the form of resource utilization and the creation of environmentally damaging waste. Therefore, there is an increasing demand for greener and more environmentally responsible methods of wafer manufacturing and manufacturing in general. Given that wafer processing is energy intensive, there is value in decoupling the growth of the semiconductor industry from its environmental impact. Increasing chip demand and increasing chip complexity are increasing resource consumption, which has environmental impacts. Summary of the Invention

[0003] The following is a simplified summary of the present disclosure to provide a basic understanding of some aspects of the disclosure. This summary is not an extensive overview of the disclosure. It does not delineate the scope or claims of particular implementations of the disclosure. Its sole purpose is to present some concepts of the disclosure in a simplified form as a prelude to the more detailed description that is presented later.

[0004] Techniques directed to an eco-efficiency monitoring and exploration platform for semiconductor manufacturing are described. In some embodiments, an engineering platform capable of supporting the engineer's development process and burden to meet both materials engineering and eco-efficiency specifications by leveraging sensor data, physics, models, algorithms, and a user interface (UI) that provides flexibility and ease in monitoring and exploring the eco-efficiency of numerous process recipes and manufacturing hardware configurations is described. One method includes receiving, by a processing device, first data indicative of an update to a substrate manufacturing system having a first configuration of manufacturing equipment and operating according to one or more process procedures. The method further includes determining, by the processing device, environmental resource data using the first data in conjunction with a digital replica. The digital replica includes a digital replica of the substrate manufacturing system. The environmental resource usage data indicative of environmental resource consumption corresponding to performing one or more process procedures by the substrate manufacturing system incorporating the update. The method further includes providing, by the processing device, the environmental resource usage data for display on a graphical user interface (GUI).

[0005] In an exemplary embodiment, the update includes replacing a first hardware subsystem device with a second hardware subsystem device having one or more operating specifications different from the first hardware subsystem device. For example, the first hardware subsystem device may include items such as a heater, a cooling module, and a gas injection, among others. In an exemplary embodiment, the update includes changing a first configuration of the manufacturing facility to a second configuration of the manufacturing facility. In an exemplary embodiment, the update includes changing a scheduled operating mode of a physical asset of the substrate manufacturing system, the scheduled operating mode including a reduced power mode. In an exemplary embodiment, the update includes changing a scheduled operating mode or hardware configuration of a support asset of the substrate manufacturing system to operate in a shared capacity. For example, the support asset operating in a shared capacity alternates performing support functions for multiple physical assets of the substrate manufacturing system that perform one or more process steps.

[0006] In an exemplary embodiment, the digital replica includes a physics-based model of one or more physical assets of the substrate manufacturing system. The physics-based model may indicate a relationship between the size and geometry of a substrate processing chamber and environmental resource consumption. The update may relate to a modification to at least one of the size or geometry of the substrate processing chamber. The physics-based model may indicate a relationship between the type of purge gas used in the substrate manufacturing system and environmental resource consumption. The update may relate to how environmental resource consumption is changed by modifying the type and amount of gas used to purge the system. For example, a particular resource consumption update may include how environmental resource consumption is affected by changing the purge gas used from a first purge gas, such as nitrogen, to clean dry air (CDA). The physics-based model may indicate a relationship between at least one of the heat extraction procedures from the substrate manufacturing system and environmental resource consumption. The update may relate to a modification to a heat rejection device, a gas abatementThe modification may involve modifications to at least one of a gas abatement device, a water cooling device, or a ventilation structure.

[0007] Aspects and implementations of the present disclosure will become more fully understood from the detailed description given below and from the accompanying drawings, which are intended to illustrate aspects and implementations by way of example, and not by way of limitation. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a block diagram illustrating an example system architecture in which implementations of the present disclosure may operate. [Figure 2] FIG. 1 is a block diagram illustrating an eco-efficiency sustainability system architecture in which implementations of the present disclosure may operate. [Figure 3] FIG. 1 is a flow diagram of an exemplary methodology for monitoring, sustaining, and / or optimizing a manufacturing process. [Figure 4A] FIG. 1 illustrates an exemplary digital replica, according to some implementations of the present disclosure. [Figure 4B] FIG. 1 illustrates an exemplary digital replica, according to some implementations of the present disclosure. [Figure 4C] FIG. 1 illustrates an exemplary digital replica, according to some implementations of the present disclosure. [Figure 4D] FIG. 1 illustrates an exemplary digital replica, according to some implementations of the present disclosure. [Figure 4E] FIG. 1 illustrates an exemplary digital replica, according to some implementations of the present disclosure. [Figure 5] FIG. 1 illustrates an example illustration of a process parameter value window, according to some implementations of the present disclosure. [Figure 6] FIG. 1 illustrates a description of a method for exploring modifications to a manufacturing process and the resulting impact on eco-efficiency, according to some implementations of the present disclosure. [Figure 7A]FIG. 1 is a flow diagram of a method related to identifying modifications to a manufacturing process, according to some implementations of the present disclosure. [Figure 7B] FIG. 1 is a flow diagram of a method related to identifying modifications to a manufacturing process, according to some implementations of the present disclosure. [Figure 7C] FIG. 1 is a flow diagram of a method related to identifying modifications to a manufacturing process, according to some implementations of the present disclosure. [Figure 8] FIG. 1 illustrates an illustration of a method for identifying modifications to a manufacturing process, according to some implementations of the present disclosure. [Figure 9A] FIG. 1 illustrates an exemplary environmental resource consumption report, according to some embodiments. [Figure 9B] FIG. 1 illustrates an exemplary environmental resource consumption report, according to some embodiments. [Figure 9C] FIG. 1 illustrates an exemplary environmental resource consumption report, according to some embodiments. [Figure 10A-Left] 1A-1C illustrate various views of an integrated environmental resource consumption dashboard graphical user interface (GUI), according to some embodiments. [Figure 10A-Middle] 1A-1C illustrate various views of an integrated environmental resource consumption dashboard graphical user interface (GUI), according to some embodiments. [Figure 10A-Right] 1A-1C illustrate various views of an integrated environmental resource consumption dashboard graphical user interface (GUI), according to some embodiments. [Figure 10B-Left] 1A-1C illustrate various views of an integrated environmental resource consumption dashboard graphical user interface (GUI), according to some embodiments. [Figure 10B-Middle] 1A-1C illustrate various views of an integrated environmental resource consumption dashboard graphical user interface (GUI), according to some embodiments. [Figure 10B-Right] 1A-1C illustrate various views of an integrated environmental resource consumption dashboard graphical user interface (GUI), according to some embodiments. [Figure 10C-Left] 1A-1C illustrate various views of an integrated environmental resource consumption dashboard graphical user interface (GUI), according to some embodiments. [Figure 10C-Right] 1A-1C illustrate various views of an integrated environmental resource consumption dashboard graphical user interface (GUI), according to some embodiments. [Figure 10D-Left] 1A-1C illustrate various views of an integrated environmental resource consumption dashboard graphical user interface (GUI), according to some embodiments. [Figure 10D-Middle] 1A-1C illustrate various views of an integrated environmental resource consumption dashboard graphical user interface (GUI), according to some embodiments. [Figure 10D-Right] 1A-1C illustrate various views of an integrated environmental resource consumption dashboard graphical user interface (GUI), according to some embodiments. [Figure 10E-Left] 1A-1C illustrate various views of an integrated environmental resource consumption dashboard graphical user interface (GUI), according to some embodiments. [Figure 10E-Middle] 1A-1C illustrate various views of an integrated environmental resource consumption dashboard graphical user interface (GUI), according to some embodiments. [Figure 10E-Right] 1A-1C illustrate various views of an integrated environmental resource consumption dashboard graphical user interface (GUI), according to some embodiments. [Figure 11] FIG. 1 is a block diagram of an exemplary computing device that operates in accordance with one or more aspects of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0009] Ecological-efficiency (eco-efficiency) characterization is a complex technique used to determine how different levels of inputs (e.g., resources, utilization, etc.) associated with a particular manufacturing tool impact the tool's eco-efficiency during use. Eco-efficiency characterization can be useful during manufacturing tool development to help develop manufacturing tools that maximize per-unit (or per-unit-time) eco-efficiency and minimize adverse environmental impacts. Eco-efficiency characterization can also be useful after tool development, when the tool is operable to fine-tune the tool's per-unit eco-efficiency characteristics by taking into account specific parameters according to which tool it is operating.

[0010] Embodiments described herein provide a system for systematically conducting eco-efficiency characterization of manufacturing tools throughout their design, development, and manufacturing processes. In some embodiments, an engineering platform is described that can support the engineer's development process and burden of meeting both materials engineering and eco-efficiency specifications by leveraging sensor data, physics, models, algorithms, and a user interface (UI) that provides the flexibility and ease of monitoring and exploring the eco-efficiency of numerous process recipes and manufacturing hardware configurations. Embodiments further provide for the integration of eco-efficiency characterization and optimization of multiple processes (e.g., cumulative consumption per semiconductor device (e.g., memory, logic, integrated circuit (IC))). Embodiments further provide for the use of eco-efficiency characterization using digital replicas associated with manufacturing processes and / or manufacturing equipment to further determine modifications and / or optimizations to reduce environmental resource consumption and / or environmental impact (e.g., per device, die, wafer, etc.). Embodiments further provide a platform for exploring manufacturing system modifications and resulting eco-efficiency impacts using digital replicas (e.g., without the need for physical testing and empirical results).

[0011] In some embodiments, eco-efficiency is calculated per unit. Typically, per-unit eco-efficiency is not considered in the manufacturing tool development process. Additionally, characterizing per-unit eco-efficiency to adjust settings on a manufacturing tool while the tool is in use (e.g., while the tool is being used for wafer production) can be a tedious and complicated process. Furthermore, previous solutions have used specialized human eco-efficiency training and specialized engineers and analysts for eco-efficiency characterization analysis. Embodiments of the present disclosure provide improved methods, systems, and software for per-unit eco-efficiency characterization. These methods, systems, and software can be used by individuals without specialized eco-efficiency training.

[0012] In one embodiment, eco-efficiency characterization can be performed by a software tool at all stages of a fabrication facility's life cycle, including during the design and operation stages of the wafer fabrication facility. Eco-efficiency can include the amount of environmental resources (e.g., electrical energy, water, gas, etc.) consumed per unit of facility production (e.g., per wafer or per device manufactured). Eco-efficiency can also be characterized as the amount of environmental impact (e.g., CO2 emissions, heavy metal waste, etc.) generated per unit of facility production.

[0013] A per-unit analysis, where the unit is any measurable quantity (e.g., substrate (wafer), die, area (cm), time period, device, etc.) operated by the manufacturing tool, allows for a more precise characterization of eco-efficiency. Eco-efficiency “per unit” allows for a precise determination of resource usage and environmental impact per unit produced and can be easily manipulated as a measure of value. For example, a particular manufacturing tool may be determined to have an electrical energy per wafer pass eco-efficiency rating of 1.0-2.0 kWh per wafer pass (in other embodiments, the eco-efficiency rating may be less than 0.5 kWh per wafer pass, up to 20 kWh, or even greater than 20 kWh), indicating that each wafer operated by the manufacturing tool may use, for example, 1.0-2.0 kWh of electrical energy per wafer pass. In other embodiments, various other amounts of electrical energy may be used. Determining eco-efficiency per wafer pass allows for easy comparison with other manufacturing tools that have different annual electrical energy consumption values ​​due to variations in annual wafer throughput. In one embodiment, eco-efficiency may also be determined per device by dividing the eco-efficiency characterization per wafer by the number of devices per wafer.

[0014] Conducting eco-efficiency characterization during the early design stages of equipment manufacturing allows designers to make better, more eco-efficient design choices at minimal cost. Eco-efficiency can be manipulated and improved early in the design stage of manufacturing equipment. Early eco-efficiency characterization in the design process can enable better, more eco-friendly component selection, subsystem design, system integration, process design, process material selection, and system configuration.

[0015] At any time in the development process for a tool, an engineer may change the configuration of the tool, which may cause a change in the eco-efficiency model for the tool. The changes to the configuration and the resulting changes to the eco-efficiency model may be stored in a database. In one embodiment, a designer may see updates to the manufacturing facility design in real time as eco-efficiency-related changes are made. The designer may select equipment or sub-components with the desired eco-efficiency for a desired application. Furthermore, eco-efficiency (e.g., per-unit eco-efficiency) may be calculated for the manufacturing facility based on known eco-efficiency characterizations (e.g., per-unit eco-efficiency characterizations) for the sub-components. Such known eco-efficiency characterizations for the sub-components may be stored in a database. In another embodiment, eco-efficiency may be calculated for the manufacturing facility's combined utility and utilization data for each of the manufacturing facility's sub-components.

[0016] Components and subcomponents can be compared and contrasted. If an eco-efficiency model does not already exist for a particular piece of equipment or subcomponent, the designer can perform an eco-efficiency analysis on the equipment and store the resulting eco-efficiency model in a database. Designers can have the option to save various versions of equipment under development, with each version having an associated eco-efficiency model. In this manner, versioning is trackable, and eco-efficiency can be optimized by determining the equipment design version with the desired eco-efficiency. In some embodiments, comparisons between components and subcomponents can be used to identify patterns, issues, and / or insights into chamber-to-chamber matching between multiple devices with multiple chamber matching. As a result of such comparisons and contrasts between components and subcomponents or versions, overall eco-efficiency performance, consumption savings such as carbon footprint, etc. can be reported.

[0017] Manufacturing equipment and subsystems are sometimes used in a variety of applications, each with its own eco-efficiency. In such situations, multiple eco-efficiency characterizations for the same equipment or sub-component used under different conditions may be stored in the database. When a designer selects an appropriate equipment from the database, the designer may be presented with various applications for the equipment, each with its own eco-efficiency characterization. The designer may modify the equipment's parameters to match the appropriate application, perform the eco-efficiency characterization, and store the results back in the database.

[0018] In another embodiment, the eco-efficiency characterization may be performed on the manufacturing facility itself during operation. The manufacturing facility may have access to real-time variables, such as equipment utilization and utility usage data, and use the real-time variables in the eco-efficiency model. In this embodiment, the manufacturing facility may fine-tune settings on the facility to maximize eco-efficiency given the current operating conditions of the manufacturing facility. The eco-efficiency characterization of the facility may be useful for fine-tuning the eco-efficiency of a manufacturing facility designed using theoretical, average, or expected variable conditions.

[0019] In some embodiments, the eco-efficiency characterization may be determined using a digital replica. A selection, including one of the manufacturing process or manufacturing equipment for performing the manufacturing operations of the first manufacturing process, may be input into the digital replica. The digital replica may include a physics-based model of the manufacturing process and / or manufacturing equipment. The physics-based model may allow for what-if scenarios in which module / subsystem consumption is estimated using a fully physics-based model or a reduced-order model (e.g., lamp heating variations conceptualized before a subsystem, such as an infrared-based lamp heating subsystem, is developed). In some embodiments, the digital replica may include other models, such as statistical models, to determine the physical conditions of the manufacturing process or manufacturing equipment (e.g., heating losses, energy consumption, etc., due to gases exiting the exhaust and / or foreline).

[0020] In some embodiments, eco-efficiency characterization may be integrated across multiple manufacturing processes. For example, cumulative consumption per device or process component may be calculated across various devices and processes to compile a cumulative eco-efficiency. Additionally or alternatively, auxiliary or support equipment (e.g., sub-manufacturing equipment) may be characterized, such as equipment shared across multiple manufacturing facilities. For example, pumps, abatement Devices and / or equipment such as abatement, heater jackets, filtration systems, or other devices not used to directly process substrates may also be monitored and characterized for eco-efficiency.

[0021] In some embodiments, modifications to a manufacturing process (e.g., a subset of processes or multiple processes) may be determined based on the environmental resource usage data or the eco-efficiency characterization. For example, the environmental resource usage data may be used as input to a machine learning model. One or more outputs from the machine learning model may be obtained that indicate the modifications to the manufacturing process and, in some embodiments, a level of confidence that the modifications satisfy a threshold condition. The modifications to the manufacturing process may be related to improving the eco-efficiency (e.g., reducing environmental resource consumption and / or environmental impact) of the manufacturing process selection.

[0022] In some embodiments, determining one or more modifications to the manufacturing process may be related to an optimization procedure for the manufacturing process. The system and / or methodology may determine multiple modifications to apply to the manufacturing process to meet certain environmental optimization requirements. For example, local regulations may impose limits on the level of usage of certain resources (e.g., electricity, water, etc.) or incentivize lower consumption practices through different reward mechanisms. The eco-efficiency systems and methodologies described herein (e.g., real-time dashboards as monitoring features) may be readily used to prepare the necessary reports as evidence of compliance, and the eco-efficiency optimization features may be used to realize savings for the manufacturing system.

[0023] In some embodiments, compliance reporting may include reporting based on generally accepted codes and / or standards, such as those published by the Semiconductor Equipment and Materials Association (SEMI) in its Semiconductor Facility Systems Guidelines for Energy, Electricity, and Productive Maintenance for Semiconductor Manufacturing Facilities (SEMI S23-0813). For example, SEMI S23-0813 provides energy conversion factors (ECFs) (e.g., energy consumption per unit of flow) of key utilities. The ECFs may estimate the energy consumption of utilities and be used to estimate energy savings in semiconductor manufacturing facilities.

[0024] In some embodiments, eco-efficiency is based on resource consumption, such as, for example, energy consumption, gas consumption (such as hydrogen, nitrogen, chemicals used for etching or depositing thin films, CDA (clean dry air)), and / or water consumption (such as process cooling water (PCW), deionized water (DIW), and ultrapure water (UPW)). However, in some embodiments, eco-efficiency is based on life cycle data of components associated with the manufacturing equipment. For example, environmental resource consumption and / or environmental impacts associated with eco-efficiency characterization may be related to replacement or maintenance procedures for consumable parts of the manufacturing equipment. Modifications may be related to maintenance procedures for consumable parts of the manufacturing equipment.

[0025] In some embodiments, characterizing and optimizing a manufacturing process may include a recipe builder methodology. The recipe builder methodology may include dynamically calculating the resource consumption and / or environmental impact of individual manufacturing steps as part of the recipe generation and / or modification process. A user may be able to add, remove, and / or modify various combinations, subcombinations, and / or sequencing of process steps and / or manufacturing equipment to perform the processing steps. The modified recipe may be analyzed to determine the environmental efficiency of the modified recipe. Performing a process may include, for example, processing wafers, transporting wafers, auxiliary / support equipment that enables the process steps, and / or other functions related to the manufacturing process.

[0026] In some embodiments, an update is received for a substrate manufacturing system having a first configuration of manufacturing equipment and operating according to one or more process procedures. The update may be used in conjunction with the digital replica to determine environmental resource data. The digital replica may include a digital replica of the substrate manufacturing system. The environmental resource usage data may indicate environmental resource consumption corresponding to performing one or more process procedures by the substrate manufacturing system incorporating the update, as described above. The environmental resource usage data may be provided for display on a graphical user interface (GUI).

[0027] In some embodiments, updating a manufacturing system may include replacing a first hardware subsystem device with a second hardware subsystem device having one or more operating specifications different from the first hardware subsystem device. In an exemplary embodiment, updating a manufacturing system may include changing a first configuration of a manufacturing facility to a second configuration of the manufacturing facility. In an exemplary embodiment, updating a manufacturing system may include changing a scheduled operating mode of a physical asset of the substrate manufacturing system, the scheduled operating mode including a reduced power mode. In an exemplary embodiment, updating includes changing a scheduled operating mode of a support asset of the substrate manufacturing system to a shared operating mode, the support asset operating in the shared operating mode alternating between performing support functions for multiple physical assets of the substrate manufacturing system that perform one or more process steps.

[0028] As previously described, the digital replica may include a physics-based model of one or more physical assets of the substrate manufacturing system. The physics-based model may indicate a relationship between the size and geometry of a substrate processing chamber and environmental resource consumption. The update may relate to a modification to at least one of the size or geometry of a substrate processing chamber. The physics-based model may indicate a relationship between the type of purge gas used in the substrate manufacturing system and environmental resource consumption. The update may relate to how environmental resource consumption is changed by modifying the type and amount of gas used to purge the system. For example, a particular resource consumption update may include how environmental resource consumption is affected by changing the purge gas used from a first purge gas, such as nitrogen, to clean dry air (CDA). The physics-based model may indicate a relationship between at least one of the heat extraction procedures from the substrate manufacturing system and environmental resource consumption. The update may relate to a heat rejection device, a gas abatement The method may involve modifications to at least one of the device, the water cooling device, or the ventilation structure.

[0029] 1 is a block diagram illustrating an example system architecture 100 in which implementations of the present disclosure may operate. As shown in FIG. 1, the system architecture 100 includes a manufacturing system 102, a data store 112, a server 120, a client device 150, and / or a machine learning system 170. The machine learning system 170 may be part of the server 120. In some embodiments, one or more components of the machine learning system 170 may be fully or partially integrated into the client device 150. The manufacturing system 102, the data store 112, the server 120, the client device 150, and the machine learning system 170 may each be hosted by one or more computing devices, including a server computer, a desktop computer, a laptop computer, a tablet computer, a notebook computer, a personal digital assistant (PDA), a mobile communication device, a cell phone, a handheld computer, or a similar computing device. As used herein, a server may refer to a server, but may also include an edge computing device, an on-premise server, a cloud, etc.

[0030] The manufacturing system 102, the data store 112, the server 120, the client device 150, and the machine learning system 170 may be coupled to each other via a network (e.g., to implement the methodologies described herein). In some embodiments, the network 160 is a private network that provides each element of the system architecture 100 with access to each other and to other privately available computing devices. The network 160 may include one or more wide area networks (WANs), local area networks (LANs), wired networks (e.g., Ethernet networks), wireless networks (e.g., 802.11 networks or Wi-Fi networks), cellular networks (e.g., Long Term Evolution (LTE) networks), cloud networks, cloud services, routers, hubs, switches, server computers, and / or any combination thereof. Alternatively or additionally, any of the elements of the system architecture 100 may be integrated together or otherwise coupled without the use of the network 160.

[0031] Client device 150 may be or include any personal computer (PC), laptop, mobile phone, tablet computer, netbook computer, network-connected television (“smart TV”), network-connected media player (e.g., Blu-ray player), set-top box, over-the-top (OOT) streaming device, operator box, etc. Client device 150 may include a browser 152, applications 154, and / or other tools as described and as implemented by other systems in system architecture 100. In some embodiments, client device 150 may be capable of accessing manufacturing system 102, data store 112, server 120, and / or machine learning system 170 and communicating (e.g., sending and / or receiving) indications of eco-efficiency, including one or more environmental resource consumption (e.g., environmental resource consumption) and / or environmental impact, and / or inputs and outputs of various process tools (e.g., component integration tool 122, digital replica tool 124, optimization tool 126, strategy builder tool 128, resource consumption tool 130, etc.) at various stages of processing of system architecture 100, as described herein.

[0032] 1, manufacturing system 102 includes tooling 104, tooling controller 106, process recipe 108, and sensors 110. Tooling 104 may be any combination of ion implanters, etch reactors (e.g., processing chambers), photolithography devices, deposition devices (e.g., for performing chemical vapor deposition (CVD), physical vapor deposition (PVD), ion-assisted deposition (IAD), etc.), or any other combination of manufacturing devices.

[0033] The process recipe 108, also referred to as a manufacturing recipe or manufacturing process instructions, includes a sequencing of machine operations with a process implementation that, when applied in a specified order, creates a manufactured sample (e.g., a substrate or wafer having predetermined properties or meeting predetermined specifications). In some embodiments, the process recipe is stored in a data store or, alternatively or additionally, in a manner that generates a table of data representing the steps or operations of the manufacturing process. Each step may store the known environmental efficiency of a given process step. Alternatively or additionally, each process step may store parameters that indicate the physical conditions required by the process step (e.g., target pressure, temperature, exhaust, energy throughput, etc.).

[0034] The equipment controller 106 may include software and / or hardware components capable of performing the steps of the process recipe 108. The equipment controller 106 may monitor the manufacturing process through sensors 110. The sensors 110 may measure process parameters to determine whether process criteria are met. The process criteria may be related to process parameter value windows (e.g., as described in connection with FIG. 5 ). The sensors 110 may include various sensors that may be used to measure consumption (e.g., power, current, etc.) (explicitly or as a measure of consumption). The sensors 110 may include physical sensors, Internet of Things (IoT), and / or virtual sensors (e.g., sensors that are not physical sensors but are based on virtual measurements based on models that estimate parameter values).

[0035] Additionally or alternatively, the equipment controller 106 may monitor eco-efficiency by measuring resource consumption (e.g., air emissions, energy consumption, process material consumption, etc.) of various process steps. In some embodiments, the equipment controller 106 determines the eco-efficiency of the associated machine equipment 104. The equipment controller 106 may also adjust settings associated with the manufacturing equipment 104 based on the determined eco-efficiency model (e.g., including determined modifications to the process recipe 108) to optimize the eco-efficiency of the equipment 104 given current manufacturing conditions.

[0036] In one embodiment, the equipment controller 106 may include a main memory (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM), static random access memory (SRAM), etc.) and / or a secondary memory (e.g., a data store device such as a disk drive (e.g., data store 112 or cloud data)). The main memory and / or secondary memory may store instructions for implementing various types of manufacturing processes (e.g., process recipe 108).

[0037] In one embodiment, the equipment controller 106 may determine an actual eco-efficiency characteristic associated with the manufacturing facility 104 based on first utility usage data associated with the manufacturing facility 104 and first usage data associated with the manufacturing facility 104. The first utility usage data and the first usage data may be determined, for example, by the equipment controller 106. In another embodiment, the first utility usage data and the first usage data are received from an external source (e.g., the server 120, a cloud service, and / or a cloud data store). The equipment controller 106 may compare the actual eco-efficiency characteristic to a first eco-efficiency characteristic associated with the manufacturing facility 104 (e.g., a first estimated eco-efficiency characteristic). The eco-efficiency characteristic may differ when usage and utilization data values ​​that differ from the actual values ​​associated with the operating manufacturing facility 104 are used to calculate the first eco-efficiency characteristic.

[0038] In one embodiment, the equipment controller 106 may determine that the first eco-efficiency characterization is more eco-efficient than the actual eco-efficiency characterization, indicating that it may be possible to adjust settings on the manufacturing equipment 104 to better optimize the manufacturing equipment 104 for eco-efficiency. In some embodiments, the manufacturing equipment 104 may control and adjust sub-component settings to better optimize eco-efficiency.

[0039] The equipment controller 106 may also determine, based on the actual usage data, the actual utilization data, and the eco-efficiency characterization, that the actual usage data or utilization data is not the same as the usage data and utilization data associated with the first eco-efficiency characterization. This may be true when nominal or estimated data values ​​are used to determine the first eco-efficiency characterization and different actual recorded data values ​​are used while the manufacturing equipment 104 is operating. In such a scenario, adjustments to one or more settings associated with the manufacturing equipment 104 may be beneficial to optimize the eco-efficiency of the manufacturing equipment.

[0040] The data store 112 may be another type of component or device capable of storing data, such as a memory (e.g., random access memory), a drive (e.g., a hard drive, a flash drive), a database system, or a store provided by a cloud server and / or processor. The data store 112 may store one or more historical sensor data. The data store 112 may store one or more of eco-efficiency data 114 (e.g., including historical and / or current eco-efficiency data), sensor and process recipe data 116 (e.g., including historical and / or current sensor and process recipe data 116), correction and optimization data (e.g., including historical and / or current correction and optimization data 118), and digital replica data 119. The sensor and process recipe data 116 may include various process steps, process parameter windows, alternative process steps, process queuing instructions, etc. for implementing multiple processes on overlapping manufacturing equipment. The sensor and process recipe data 116 may be linked to or otherwise associated with the eco-efficiency data 114 to track eco-efficiency across various process steps, recipes, etc. The correction and optimization data 118 may include historical corrections made to previous process recipes (including individual process steps or collections of multiple process recipes) and the associated eco-efficiency changes that resulted from the corrections.

[0041] The eco-efficiency data 114 may include various consumed resources used in the eco-efficiency characterization. In one embodiment, the eco-efficiency data 114 incorporates one or more of water usage, emissions, electrical energy usage, and any combination thereof. In other embodiments, the eco-efficiency data 114 may include resource consumption for other categories, such as gas usage, heavy metal usage, and eutrophication potential.

[0042] Digital replica data 119 may include data related to the digital replica. Digital replica data 119 may include data related to a digital twin. As used herein, a digital twin may include a digital replica of a physical asset, such as manufacturing equipment 104. The digital twin includes characteristics of the physical asset at each stage of the manufacturing process, including, but not limited to, coordinate axis dimensions, weight characteristics, material characteristics (e.g., density, surface roughness), electrical characteristics (e.g., conductivity), optical characteristics (e.g., reflectivity), etc.

[0043] As previously described, the digital replica may include physics-based models of one or more physical assets of the substrate manufacturing system. The digital replica data 119 may encapsulate relationships, parameters, specifications, etc. related to one or more aspects of the physics-based model. For example, the physics-based model may indicate a relationship between the size and geometry of a substrate processing chamber and environmental resource consumption. An update may relate to a modification to at least one of the size or geometry of a substrate processing chamber. The physics-based model may indicate a relationship between the type of purge gas used in the substrate manufacturing system and environmental resource consumption. An update may relate to how environmental resource consumption is changed by modifying the type and amount of gas used to purge the system. For example, a particular resource consumption update may include how environmental resource consumption is affected by changing the purge gas used from a first purge gas, such as nitrogen, to clean dry air (CDA). The physics-based model may indicate a relationship between at least one of the heat extraction procedures from the substrate manufacturing system and environmental resource consumption. An update may relate to a heat rejection device, a gas abatement The method may involve modifications to at least one of the device, the water cooling device, or the ventilation structure.

[0044] Server 120 may include a component integration tool 122, a digital replica tool 124, an optimization tool 126, a strategy builder tool 128, a resource consumption tool 130, and / or an exploration tool. Component integration tool 122 may determine cumulative consumption per device (e.g., per individual manufacturing facility). The various tools of server 120 may communicate data among each other to perform their respective functions as described herein.

[0045] Component integration tool 122 may receive manufacturing data (e.g., recipes, recipe selections, manufacturing equipment, inter-recipe and intra-recipe processes, etc.) and perform eco-efficiency analysis across various sections of the data. In some embodiments, component integration tool 122 may determine eco-efficiency characterizations across multiple process steps from individual process recipes. For example, component integration tool 122 may determine eco-efficiency characterizations across all steps of a chip manufacturing process from start to finish. For example, each manufacturing step may include one or more manufacturing steps (e.g., hundreds of manufacturing steps), each with its own eco-efficiency characterization and, together, a joint eco-efficiency characterization. In another example, process selection may be used to determine the eco-efficiency of a subset of manufacturing process steps.

[0046] In another embodiment, component integration tool 122 may perform an inter-recipe process eco-efficiency characterization. For example, the eco-efficiency characterization may relate to a manufacturing device (e.g., of manufacturing system 102) that performs multiple different process steps from multiple different manufacturing processes (e.g., process recipe 108). In another example, the sequencing of various process steps (e.g., within a recipe or between recipes) may affect the overall eco-efficiency. Component integration tool 122 may perform an overall eco-efficiency characterization across the systems and / or process sequences of the manufacturing device. For example, component integration tool 122 may perform an eco-efficiency comparison between subcomponents (e.g., multiple processing chambers) that perform similar functions.

[0047] In an illustrative example, each process step, such as epitaxial deposition or etching, may be performed by a processing chamber. Each of these is performed using a process recipe. There may be many different process recipes for performing a process, such as epitaxial deposition. For example, a process recipe may include multiple steps, such as 1) purging the chamber, 2) pumping, 3) flowing gases, and 4) heating the chamber. These steps may be associated with one or more process recipes.

[0048] In another embodiment, component integration tool 122 may perform eco-efficiency characterization, including eco-efficiency of auxiliary equipment. Auxiliary equipment may include equipment not directly used for manufacturing but that supports carrying out various process strategies. For example, auxiliary equipment may include a substrate transport system designed to move wafers between various manufacturing devices. In another example, auxiliary equipment may include heat sinks, shared exhaust vents, power supply systems, etc. Component integration tool 122 may consider auxiliary device resource consumption and combine auxiliary device resource consumption with manufacturing resource consumption to determine resource consumption for a process strategy (e.g., a subset or all strategies) or a combination of strategies (e.g., a subset or all strategies).

[0049] In another embodiment, component integration tool 122 may perform an eco-efficiency characterization that considers a sequence of processes or strategies. For example, performing process step A followed by process step B may result in a first resource consumption, while performing process step B followed by process step A may result in a second resource consumption that differs from the first resource consumption. Component integration tool 122 integrates eco-efficiency across multiple pieces of equipment and / or process steps and considers the sequence of process steps for a process strategy (e.g., a subset or all strategies) or a combination of strategies (e.g., a subset or all strategies).

[0050] In some embodiments, there is a different manufacturing facility for each process step. For example, a film on a wafer may have multiple layers. A first machine may perform a first operation (e.g., deposition), a second machine may perform a second operation (e.g., etch), a third machine may perform a third operation (e.g., deposition), and so on. The component integration tool 122 may instruct the resource consumption tracker to track multiple processing steps across multiple machines and generate a data archival report. As previously mentioned, the consumption report may be plotted for selection of a processing strategy, including the life of the wafer from start to finish.

[0051] In some embodiments, the component integration tool 122 may perform a chamber-to-chamber environmental resource consumption comparison. The component integration tool may utilize the digital replica tool 124 to provide one or more physical data that provides a rationale for the difference in environmental efficiency between the two chambers.

[0052] The digital replica tool 124 receives manufacturing data from the manufacturing system 102 and / or the client device 150 and generates a digital replica related to the manufacturing data. The manufacturing data may include the selection of process steps for the tooling 104 and the process recipe 108. The digital replica tool 124 generates a digital twin of the physical system architecture of the manufacturing system or a virtual input system (e.g., generated by a user on the client device 150).

[0053] The digital replica generated by the digital replica tool 124 may include one of a physics model, a statistical model, and / or a hybrid model. The physics model may include physics-based constraints and control algorithms designed to estimate the physical conditions of the input manufacturing data (e.g., exhaust temperature, power supply requirements, and / or other conditions indicative of the physical environment related to environmental resource consumption). For example, a user may create a process recipe on the client device 150. The process recipe may include parameters for the process or recipe and instructions for using mechanical equipment in a specific manner. The digital replica tool 124 takes this manufacturing data and determines the physical constraints of the system (e.g., operating temperature, pressure, exhaust parameters, etc.). For example, the physics model may identify the physical conditions of the system based on the chamber hardware configuration (e.g., whether to use Type A or Type B equipment materials) and / or recipe parameters. In another example, the physical conditions may be determined from the associated mechanical equipment components that affect water, air, and / or heat loss to heating, ventilation, and air conditioning (HVAC) equipment. The digital replica tool 124 may cooperate with other tools (e.g., the component integration tool 122 and / or the resource consumption tool 130) to predict an eco-efficiency characterization of the received manufacturing data. Note that the digital replica tool 124 may predict the eco-efficiency of a manufacturing process and manufacturing equipment selection without receiving empirical data from implementing the process strategy with the manufacturing facility 104. Thus, the digital replica of the manufacturing facility may be used to predict the eco-efficiency of an equipment design and / or process strategy without actually building the particular equipment design or implementing the particular process strategy.

[0054] In some embodiments, the digital replica tool 124 can operate in conjunction with a digital twin. As used herein, a digital twin is a digital replica of a physical asset, such as a manufactured part. The digital twin includes characteristics of the physical asset at each stage of the manufacturing process, including, but not limited to, coordinate axis dimensions, weight characteristics, material characteristics (e.g., density, surface roughness), electrical characteristics (e.g., conductivity), and optical characteristics (e.g., reflectivity), among others.

[0055] In some embodiments, the physics-based models used by the digital replica tool 124 may include fluid flow modeling, gas flow and / or consumption modeling, chemistry-based modeling, heat transfer modeling, electrical energy consumption modeling, plasma modeling, etc. Figures 4A-4E outline various physics-based models that may be utilized by the digital replica tool 124.

[0056] In some embodiments, the digital replica tool 124 may predict the eco-efficiency of the manufacturing data using statistical modeling. Statistical models may be used to process the manufacturing data based on previously processed historical eco-efficiency data (e.g., eco-efficiency data 114) using statistical operations to validate, predict, and / or transform the manufacturing data. In some embodiments, statistical models are generated using statistical process control (SPC) analysis to determine control limits for the data and identify the data as more or less reliable based on those control limits. In some embodiments, statistical models relate to univariate and / or multivariate data analysis. For example, various parameters may be analyzed using statistical models to determine patterns and correlations through statistical processes (e.g., range, minimum, maximum, quartiles, variance, standard deviation, etc.). In another example, relationships between multiple variables may be identified using regression analysis, path analysis, factor analysis, multivariate statistical process control (MCSPC), and / or multivariate analysis of variance (MANOVA).

[0057] The optimization tool 126 may receive the process recipe 108 and the machinery 104 selections and may identify modifications to the selections to improve eco-efficiency (e.g., reduce resource consumption, resource cost consumption, and / or environmental impact (e.g., gases or particulate nuclides entering the atmosphere)). The optimization tool 126 may incorporate the use of a machine learning model (e.g., model 190 of machine learning system 170). The machine learning model may receive the process recipe and / or machinery selections as input and determine one or more modifications to the selections that, when implemented by the manufacturing system 102, improve the overall eco-efficiency of the selections. In some embodiments, the machine learning model may use a digital replica tool to generate synthetic manufacturing data for training. Alternatively or additionally, the machine learning model may use historical data (e.g., eco-efficiency data 114, sensor and process recipe data 116, and / or modification and optimization data 118) to train the machine learning model.

[0058] The modifications identified by the optimization tool 126 may include changing process steps, altering the order of processes, changing parameters performed by a piece of tooling, changing the interaction (e.g., order, simultaneous operations, delay times, etc.) between a first process recipe and a second process recipe, etc. In some embodiments, the optimization tool 126 may send instructions to the manufacturing system 102 to perform the optimization directly. However, in other embodiments, the optimization tool may display the modifications on a graphical user interface (GUI) for an operator to manipulate. For example, the digital replica tool 124 may send one or more modifications to the client device 150 for display in the browser 152 and / or application 154.

[0059] In some embodiments, optimization tool 126 may adjust hyperparameters of the digital twin model generated by digital replica tool 124. As described in later embodiments, optimization tool 126 may incorporate reinforcement learning and / or deep learning by performing simulated modifications on the digital replica and evaluating eco-efficiency results output from the digital replica.

[0060] In some embodiments, optimization tool 126 may perform an eco-efficiency characterization and optimization that prioritizes one or more types of environmental resources. For example, as previously described, the eco-efficiency characterization may be based on various resource consumptions, such as water usage, gas usage, and energy usage. Optimization tool 126 may perform an optimization that prioritizes a first resource consumption (e.g., water usage) over a second resource consumption (e.g., gas usage). In some embodiments, optimization tool 126 may perform an optimization using a weighted priority system. For example, when optimizing eco-efficiency and / or identifying eco-efficiency modifications to a manufacturing process, one or more resource consumptions may be assigned a weight that indicates an optimization priority for the associated per-unit resource consumption.

[0061] The strategy builder tool 128 may receive a manufacturing process and / or equipment selection and dynamically determine and predict eco-efficiency, step by step, after each addition, deletion, and / or modification to the virtual manufacturing process and / or equipment selection. The strategy builder tool 128 may use other tools (e.g., the component integration tool 122, the digital replica tool 124, the optimization tool 126, and the resource consumption tool 130) to dynamically update the determined eco-efficiency when the manufacturing strategy is updated. For example, a user may create a manufacturing strategy. The strategy builder tool 128 may output a current eco-efficiency of the current iteration of the process strategy. The strategy builder tool 128 may receive modifications to the current iteration that updated the process strategy. The strategy builder tool 128 may output an updated eco-efficiency characterization.

[0062] In some embodiments, the strategy builder tool 128 and the optimization tool 126 may be used to identify one or more strategies as being more eco-efficient than others. For example, the strategy builder tool 128 may cause or otherwise result in the presentation on a GUI (e.g., client device 150) of one or more (e.g., the top three) of the most energy-efficient strategies associated with the process tool. The strategy builder tool 128 may use the digital replica tool 124 to provide details illustrating the rationale for why one or more energy-efficient strategies are performing well at a correspondingly high eco-efficiency.

[0063] The resource consumption tool 130 may track various resource consumptions. For example, as previously mentioned, environmental characterization may be based on broader resources such as energy consumption, gas emissions, and water usage. However, the resource consumption tool 130 may track resource consumption more specifically. In some embodiments, a selection of process strategies and / or manufacturing equipment is received by the resource consumption tool 130. The resource consumption tool 130 may determine component life cycle data relevant to the selection of manufacturing equipment and / or process strategies. For example, manufacturing equipment wears out with use and, in some cases, requires corrective action, such as replacing and / or repairing components. This corrective action also relates to environmental consumption (e.g., resource consumption for which the corrective action should be implemented). The resource consumption tool 130 may track component life data individually and provide per-unit environmental resource consumption and / or environmental impact based on expected future corrective actions to be implemented.

[0064] In some embodiments, environmental resource consumption may be monitored, tracked, and / or otherwise determined across various breakdowns. In some embodiments, the resource consumption tool 130 may perform live monitoring of energy, gas, and water consumption. The resource consumption tool 130 may determine chamber-level consumption, including calculating the total electrical, gas, and water consumption of a chamber (e.g., per wafer, per day, per week, per year, etc.). The resource consumption tool 130 may determine tool-level consumption, including determining the total electrical, gas, and water consumption of a tool (e.g., per day, per week, per year, etc.). The resource consumption tool 130 may determine individual gas consumption, including calculating the breakup of individual gas consumption (e.g., per wafer, per day, per week, per year, etc.). The resource consumption tool 130 may generate standard reports including chamber- and tool-level energy, gas, and water consumption.

[0065] In some embodiments, the resource consumption tool 130 may determine the total electricity, gas, and water consumption of all sub-manufacturing components (e.g., per day, per week, per year, etc.). The resource consumption tool may determine strategy-level consumption, including the total electricity, gas, and water consumption of any strategies executed on the corresponding chamber and / or tool. The resource consumption tool may determine component-level consumption, including the breakdown of energy consumption for all energy-consuming components in the chamber. The resource consumption tool 130 may perform on-demand customized reporting, including determining on-demand customized information, and on-demand customized eco-efficiency reporting. The resource consumption tool 130 may perform comparisons between time points, including quantifying energy consumption and / or energy savings for different strategies, and energy savings opportunities using strategy optimization (e.g., using the optimization tool 126).

[0066] The exploration tool 132 may communicate with the digital replica tool 124 in determining the impact of one or more updates to the manufacturing equipment 104. The exploration tool 132 may utilize the digital replica tool 124 to generate a digital replica including a digital replica of a substrate manufacturing system (e.g., the manufacturing equipment 104). The exploration tool may receive manufacturing equipment updates and enable a user to explore various alternative arrangements of the equipment used, equipment configurations, and process parameters related to equipment performance, among other things. The exploration tool 132 may use the resource consumption tool 130 to determine environmental resource usage data corresponding to performing one or more process steps with a substrate manufacturing system incorporating updates as described herein. The environmental resource usage data may be provided for display on a graphical user interface (GUI) (e.g., on the client device 150).

[0067] In some embodiments, updating a manufacturing system may include replacing a first hardware subsystem device with a second hardware subsystem device having one or more operating specifications different from the first hardware subsystem device. In an exemplary embodiment, updating a manufacturing system may include changing a first configuration of a manufacturing facility to a second configuration of the manufacturing facility. In an exemplary embodiment, updating a manufacturing system may include changing a scheduled operating mode of a physical asset of the substrate manufacturing system, the scheduled operating mode including a reduced power mode. In an exemplary embodiment, updating includes changing a scheduled operating mode of a support asset of the substrate manufacturing system to a shared operating mode, the support asset operating in the shared operating mode alternating between performing support functions for multiple physical assets of the substrate manufacturing system that perform one or more process steps.

[0068] In some embodiments, the digital twin may be used to estimate the lifespan of some consumables associated with a manufacturing process step. The lifespan data may be used to estimate lifespan duration and predict the next corrective steps to be taken in response to the predicted lifespan. For example, the lifespan data may be used to maintain optimized eco-efficiency performance by proactively notifying the supply chain about replacement part orders.

[0069] In some embodiments, the environmental resource usage data determined by other tools of the server may include environmental resource consumption and / or environmental impacts associated with one of replacement procedures or maintenance procedures for consumable parts of the first manufacturing equipment. In some embodiments, the optimization tool 126 may determine modifications to the manufacturing process that may include implementing corrective actions associated with components of machine equipment (e.g., machine equipment 104).

[0070] The probing tool 132 may perform a cost of ownership analysis associated with the manufacturing system. The cost of ownership analysis may include a comprehensive analysis into the interworkings of the manufacturing system to calculate the total cost to own and / or operate the system. The probing tool 132 may calculate the cost for a customer to perform a particular manufacturing procedure. The probing tool 132 may determine wafer costs, costs corresponding to gases used by the system, costs associated with the tools being used (e.g., life degradation data), and electricity for performing one or more process procedures by the manufacturing system. The cost of ownership may be calculated per unit (e.g., per wafer).

[0071] In some embodiments, machine learning system 170 further includes server machine 172, server machine 180, and / or server machine 192. Server machine 172 includes dataset generator 174 capable of generating datasets (e.g., a set of data inputs and a set of target outputs) for training, validating, and / or testing machine learning model 190. Some operations of dataset generator 174 are described in more detail below with respect to Figures 7A-7C.

[0072] Server machine 180 includes a training engine 182, a validation engine 184, and / or a test engine 186. An engine (e.g., training engine 182, validation engine 184, and / or test engine 186) may refer to hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, processing device, etc.), software (such as instructions executing on a processing device, a general-purpose computer system, or a dedicated machine), firmware, microcode, or a combination thereof. Training engine 182 may be capable of training machine learning models 190 using one or more sets of features associated with a training set from dataset generator 174. Training engine 182 may generate one or more trained machine learning models 190, where each trained machine learning model 190 may be trained based on a distinct set of features of the training set and / or a distinct set of labels of the training set. For example, a first trained machine learning model may have been trained using resource consumption data output by the digital replica tool 124, a second trained machine learning model may have been trained using historical eco-efficiency data (e.g., eco-efficiency data 114), etc.

[0073] The validation engine 184 may be capable of validating the trained machine learning model 190 using a validation set from the dataset generator 174. The test engine 186 may be capable of testing the trained machine learning model 190 using a test set from the dataset generator 174.

[0074] The machine learning model(s) 190 may refer to one or more trained machine learning models created by the training engine 182 using a training set that includes data inputs and, in some embodiments, corresponding target outputs (e.g., correct answers for each training input). Patterns in the dataset that cluster the data inputs and / or map the data inputs to the target outputs (correct answers) may be discovered, and the machine learning model 190 captures these patterns, is provided with the mapping, and / or learns the mapping. The machine learning model(s) 190 may include artificial neural networks, deep neural networks, convolutional neural networks, recurrent neural networks (e.g., long short-term memory (LSTM) networks, convLSTM networks, etc.), and / or other types of neural networks. The machine learning model 190 may additionally or alternatively include other types of machine learning models, such as those that use one or more of linear regression, Gaussian regression, random forests, support vector machines, etc.

[0075] The modification identification component 194 may provide the current data to the trained machine learning model 190 and may run the trained machine learning model 190 on the input to obtain one or more outputs. The modification identification component 194 may be capable of making decisions and / or performing actions from the output of the trained machine learning model 190. The ML model output may include confidence data indicating a level of confidence that the ML model output (e.g., modifications and optimization parameters) corresponds to modifications that, when applied, improve the overall eco-efficiency of the manufacturing process and / or manufacturing equipment selection. The modification identification component 194, in some embodiments, may implement process recipe modifications based on the ML model output. The modification identification component 194 may provide the ML model output to one or more tools of the server 120.

[0076] The confidence data may include or indicate a level of confidence that the ML model output is correct (e.g., the ML model output corresponds to a known label associated with the training data item). In one example, the level of confidence is a real number between 0 and 1, inclusive, where 0 indicates no confidence that the ML model output is correct and 1 indicates absolute confidence that the ML model output is correct. In response to the confidence data indicating a level of confidence below a threshold level for a predetermined number of instances (e.g., a percentage of instances, a frequency of instances, a total number of instances, etc.), the server 120 may cause the trained machine learning model 190 to be retrained.

[0077] For purposes of explanation and not limitation, embodiments of the present disclosure describe training a machine learning model that uses process recipe data and inputs current selections of manufacturing processes and / or manufacturing equipment into the trained machine learning model to determine ML model outputs (process modifications and optimization parameters, such as target eco-efficiencies for a particular resource consumption). In other implementations, heuristic or rule-based models are used to determine the outputs (e.g., without using a trained machine learning model).

[0078] In some embodiments, the functionality of manufacturing system 102, client device 150, machine learning system 170, data store 112, and / or server 120 may be provided by fewer machines. For example, in some embodiments, server machines 172 and 180 may be combined into a single machine, while in some other embodiments, server machine 172, server machine 180, and server machine 192 may be combined into a single machine. In some embodiments, server 120, manufacturing system 102, and client device 150 may be combined into a single machine.

[0079] In general, functions described in one embodiment as being performed by manufacturing system 102, client device 150, and / or machine learning system 170 may also be performed on server 120 in other embodiments, where appropriate. In addition, functionality attributed to a particular component may be performed by different or multiple components operating together. For example, in some embodiments, server 120 may receive manufacturing data and perform machine learning operations. In another example, client device 150 may perform manufacturing data processing based on output from a trained machine learning model.

[0080] Additionally, the functionality of a particular component may be performed by different or multiple components working together. One or more of the server 120, the manufacturing system 102, or the machine learning system 170 may be accessed as a service offered to other systems or devices through an appropriate application programming interface (API).

[0081] In embodiments, a "user" may be represented as a single individual. However, other embodiments of the present disclosure encompass a "user" being an entity controlled by multiple users and / or automated sources. For example, a set of individual users federated as a group of administrators may be considered a "user."

[0082] FIG. 2 is a block diagram illustrating an eco-efficiency and sustainability system architecture 200 in which implementations of the present disclosure may operate. The system architecture 200 includes a selection of processing tools 202 having one or more subcomponents 204 (e.g., processing chambers). The system architecture may further include support equipment, such as power supplies, pumps, airflow, and coolant flow, that support the one or more subcomponents. As previously described, the processing tools 202 include various manufacturing tools used to process substrates. In line 206, sensors measure and transmit manufacturing data (e.g., energy consumption sensor data, gas and water consumption data, etc.) to a common ground architecture 208. The common ground architecture 208 may include one or more control algorithms configured to perform manufacturing process steps and manage process parameters (e.g., critical process parameters, machine equipment diagnostic parameters, or parameters otherwise indicative of the manufacturing process).

[0083] The common ground architecture 208 may transmit sensor data (e.g., from wired sensors and / or wireless sensors, such as Internet of Things (IoT) sensors) to a data management algorithm (e.g., integration algorithm 210). The integration algorithm 210 may parse the manufacturing data received from the processing tool 202 and select a portion of the data for performing an eco-efficiency characterization. The integration algorithm 210 extracts data to perform a cumulative eco-efficiency characterization for a selection of manufacturing process steps and / or manufacturing equipment. The selected data may be used in conjunction with a physics-based model 214 to determine the physical conditions of the processing tool 202 (e.g., of each subcomponent 204). The data may be combined (e.g., at line 218) with scheduling information from an onboard sequencer and / or planner or from an operator. The scheduling information may include data indicative of upcoming measures, tool idle status, maintenance, etc. In some embodiments, one or more of the data and / or models described herein may be integrated in one or more of a manufacturing central scheduler network or software system, or a manufacturing execution system.

[0084] The selection of manufacturing data combined with the scheduling data is input into a physics-based model 214. In some embodiments, the physics-based model is a mechanistic model. The mechanistic model examines the mechanics of the individual data points of the manufacturing data and the scheduling information, as well as the manner in which the individual data points are combined, to determine a physical / mechanistic representation of the data combination. In some embodiments, the mechanistic model may include processing the data to determine a prediction of resource consumption. For example, the mechanistic model may process the manufacturing data to determine a resource consumption (e.g., water, energy, gas, etc.) prediction and / or an environmental impact (e.g., gases or particulate species entering the atmosphere). The mechanistic model may be generated using historical manufacturing data and later used against current data to determine a prediction.

[0085] In some embodiments, the physics-based model(s) 214 may incorporate various physics relationships, such as thermodynamics, fluid mechanics, energy conservation, gas laws, mechanical systems, energy conservation, transportation, and supply. For example, a processing tool may include a cooling water flow to a portion of a manufacturing facility device to perform a cooling process. The physics model may combine fluid mechanics with heat transfer to determine a model for converting raw manufacturing data into system process data that can be characterized for its eco-efficiency. In some embodiments, the physics model may be used to determine whether a threshold resource consumption condition is met. By the same example, the physics model may be used to determine the flow rate and heat transfer rate of a fluid within a subcomponent. If this heat transfer rate falls below a threshold, additional energy may be lost to exhaust. Thus, the physics model may determine that the fluid flow rate is operating below a desired flow rate level to maintain a desired level of eco-efficiency. Additional details regarding the physics-based model(s) 214 are described in connection with FIGS. 4A-4E.

[0086] In some embodiments, the physics-based model(s) 214 incorporate auxiliary or peripheral equipment operating resource consumption, such as the energy consumption of powering processing devices to provide control algorithms to the processing tools 202 (e.g., using the common ground architecture 208). The auxiliary equipment may not be located in close proximity to the manufacturing equipment and may not be directly related to a single manufacturing process, but may be allocated as a contribution to various manufacturing process steps (or individual manufacturing processes) using the physics-based model(s) 214.

[0087] In some embodiments, in addition to or as an alternative to using physical models, statistical models are used on the manufacturing data. Statistical models may be used to process data based on statistical operations to validate, predict, and / or transform the manufacturing data. In some embodiments, statistical models are generated using statistical process control (SPC) analysis to determine control limits for the data and identify the data as more or less reliable based on those control limits. In some embodiments, statistical models involve univariate and / or multivariate data analysis. For example, various parameters may be analyzed using statistical models to determine patterns and correlations through statistical processes (e.g., range, minimum, maximum, quartiles, variance, standard deviation, etc.). In another example, relationships between multiple variables may be identified using regression analysis, path analysis, factor analysis, multivariate statistical process control (MCSPC), and / or multivariate analysis of variance (MANOVA).

[0088] In some embodiments, system architecture 200 includes an adaptive optimization algorithm 216. The adaptive optimization algorithm 216 cooperates with the physics-based model(s) 214 to determine modifications to the manufacturing process and / or the selection of manufacturing equipment that performs the associated processes. In some embodiments, the adaptive optimization algorithm outputs automatic optimization commands to control the software (e.g., at line 224). In other embodiments, the adaptive optimization algorithm may output suggestions to an operator to optimize performance (e.g., at line 220). In some embodiments, the adaptive optimization algorithm outputs automatic optimization commands for hardware components (e.g., at line 222).

[0089] In some embodiments, the adaptive optimization algorithm 216 uses a machine learning model to determine modifications to the manufacturing process and / or manufacturing equipment. The machine learning model may be a trained machine learning model (e.g., trained and executed using methods 700A-C). As described in further embodiments, the machine learning model may operate in conjunction with a physics-based model to identify modifications to the manufacturing process and / or equipment received as input.

[0090] System architecture 200 may include an integrated dashboard GUI 212. The integrated dashboard GUI may be designed to display relevant manufacturing data (e.g., sensor data, machine diagnostics, machine status, manufacturing process status, etc.). In some embodiments, the integrated dashboard GUI includes a method for receiving input from a user. For example, a user may input manufacturing data (e.g., using the strategy builder tool 128) to generate a strategy. This additional manufacturing data may be used as input to one or more of the physics-based model(s) and adaptive optimization algorithms 216. In an embodiment, the dashboard includes a fleet view, a tool view, an equipment view, and a system schematic showing various data with charts showing corresponding environmental resource consumption. FIGS. 10A-10E describe further features, aspects, and / or details related to integrated dashboard GUI 212.

[0091] 3 shows a flow diagram of an example methodology 300 for monitoring, sustaining, and / or optimizing a manufacturing process. The example methodology can be divided into two parts: first, training a machine learning model 324, and second, implementing a manufacturing process 304. The example methodology 300, in one embodiment, includes a machine learning model 302, tool software 306, tool hardware 308, and a physics model 312.

[0092] In some embodiments, the machine learning model receives a selection of a manufacturing process and / or manufacturing equipment and outputs one or more modifications to the manufacturing process and / or manufacturing equipment to improve environmental efficiency (e.g., reduce resource consumption). In some embodiments, the physics model 312 is used to generate simulated training / validation data 320 (e.g., using method 700A). In response to the received simulated training / validation data 320, the machine learning model 302 generates simulated modifications 318 that can be returned to the physics model 312 and validated. The machine learning model 302 is trained on various simulated and / or actual training / validation data 320. Once trained, the machine learning model 302 may receive a selection of an empirical manufacturing system and / or process strategy to be implemented by the system. The machine learning model 302 outputs manufacturing process instructions and / or modifications 304 to an equipment controller (e.g., equipment controller 106) implementing the tool software 306. These modifications may improve environmental efficiency. The tool software 306 provides the manufacturing process instructions 314 to the tool hardware 308. The tool hardware 308 performs the manufacturing process and includes sensors that report sensor data to an equipment controller that implements the tool software 306.

[0093] In some embodiments, the equipment controller identifies one or more physical conditions of the tool hardware as violating a threshold condition (e.g., high temperature, overpressure, gas leak, power shortage, etc.). The equipment controller may modify the manufacturing process instructions to remedy the violated threshold condition (e.g., based on output from the machine learning model 302).

[0094] The manufacturing system, including the tool hardware 308, reports the empirical training / validation 310 to the physics model 312. The physics model may then generate and update simulated training / validation data 320, which may be updated and used for further training of the machine learning model.

[0095] In some embodiments, the physics model(s) 312 generate simulated training / validation data, while in other embodiments, the physics model(s) output modifications to the manufacturing process. In such embodiments, a machine learning model may be used as an optimization model to adjust hyperparameters (e.g., manufacturing data parameters) to identify modifications to further optimize the manufacturing process. For example, the manufacturing process may be used as input to the physics model 312. The machine learning model may then process the output of the physics model 312 to identify possible changes to the manufacturing process (i.e., hyperparameters). The identified changes may be run against the physics model to determine corresponding updated eco-efficiencies. This may be repeated in an iterative process to fine-tune the equipment design and / or policy design. In one example, the optimization model may be generated and / or implemented using an instance of the Broyden-Fletcher-Goldfar-Buschanno (BFGS) algorithm, a conjugate gradient (CG) algorithm, an instance of the Nelder-Mead algorithm, and / or a model predictive control (MPC) algorithm.

[0096] 4A-4E show example digital replicas 400A-E according to some implementations of the present disclosure. Digital replica 400 may include a digital twin of a selection of manufacturing systems, e.g., a digital replication of a manufacturing system including the same chambers, valves, gas distribution lines, materials, chamber components, etc. Digital replica 400 can receive manufacturing facility processing data (e.g., sensor data) 404A-C and process recipe 404D as inputs and output manufacturing system physical conditions 406. In some embodiments, digital replica 400 includes physics-based models that can incorporate various physics relationships, such as thermodynamics, fluid dynamics, energy conservation, gas laws, mechanical systems, energy conservation, transportation, and supply.

[0097] For example, as seen in FIG. 4A , digital replica 400A receives as input a first gas flow of a first gas 404A, a second gas flow of a second gas 404B, and a third gas flow of a third gas 404C, as well as a first process strategy 404D. The digital replica uses a physics-based model to estimate the amount of energy exiting the chamber due to the gas flows. For example, the model determines the temperature of the exhaust and the total energy flow through the exhaust. In another example, the same digital replica 400 may output eco-efficiency optimization modifications, such as a different hardware configuration for the chamber (e.g., whether to use a first line type A or a second line type B). The digital replica may identify relevant parts of the system that affect heat loss to water, air, and HVAC, and identify suggested optimizations to improve energy conservation.

[0098] In some embodiments, the digital replica 400A can include determining exhaust for one or more gas panels or gas boxes containing gases used in one or more locations throughout the manufacturing system. For example, each gas box may use a dedicated exhaust using negative pressure to effectively vent gases, such as in the event of a gas line leak or, more generally, a malfunction (e.g., to prevent toxins from entering undesired locations in the manufacturing facility or manufacturing system). The digital replica can be part of a digital twin that utilizes information about the possible types and volumes of gases in the gas box to determine adjustments to exhaust flow needed to properly dispose of the gas (e.g., vent leaks). The exhaust flow rate can be determined taking into account exhaust pressure and flow. The exhaust flow can include determining related parameters, such as optimizing environmental efficiency while maintaining minimum safety thresholds and / or standards.

[0099] In some embodiments, the digital replica 400A may indicate the temperature of the exhaust and the total energy flow through the exhaust based on the heating within the process chamber. For example, the process chamber may include one or more pieces of process equipment, such as a substrate pedestal, during a substrate processing procedure. Excess heat from within the chamber may be transferred through the exhaust. abatement The operation of the pedestal can be varied to reduce heat lost through exhaust. Several methods have been reported for controlling heat transfer in a heat transfer assembly, such as a pedestal for supporting a substrate, including both a heating element and a cooling element that removes excess heat by circulating a cooling medium, such as a gas or liquid coolant, within the pedestal or between the substrate and the pedestal. During a process, when the substrate temperature increases beyond a set range, the heating element is turned off and the cooling element is activated to remove the excess heat, thereby controlling the temperature. One or more parameters related to this process can be used as inputs to the digital replica 400A to determine how much excess heat is lost through exhaust.

[0100] In some embodiments, the digital replica 400A may include a plurality of energy flows and / or missing precursors or abatement or chemicals, including by-products of reactions exiting a scrubber system. For example, gas effluent streams from the manufacture of electronic materials, devices, products, solar cells, and memory articles (hereinafter "electronic devices") may involve decomposition products of a wide variety of chemical compounds, organic compounds, oxidizers, photoresists, and other reagents, as well as other gases and suspended particulates that may desirably be removed from the effluent stream before it is discharged from the process facility to the atmosphere.

[0101] abatementThe effluent streams to be analyzed may include nuclides produced by the electronic device manufacturing process and / or nuclides delivered to the electronic device manufacturing process and passed through the process chamber without chemical alteration. As used herein, the term "electronic manufacturing process" refers to all processing and unit operations in the manufacture of electronic devices, as well as all operations involving the handling or processing of materials used in or produced by an electronic device and / or LCD manufacturing facility, and active manufacturing (e.g., conditioning process equipment, purging chemical supply lines in preparation for an operation, etching cleanup of process tool chambers, and the release of toxic or hazardous gases from effluents produced by an electronic device and / or LCD manufacturing facility). abatement "(i) shall be construed broadly to include all activities performed with respect to an electronic device and / or LCD manufacturing facility that do not involve the use of electronic devices or LCD panels, including but not limited to the use of electronic devices or LCD panels, or the like."

[0102] In some embodiments, digital replica 400A accounts for the exhaust flow of leaking gas or as part of a cleaning procedure. For example, gas may be periodically flushed from a manufacturing asset to increase the asset's lifespan, improve product performance, or prepare the product for a different function it has been tasked with performing. Digital replica 400 may determine the environmental consumption (e.g., energy consumption, gas consumption) associated with performing this purge procedure. For example, digital replica 400A may show the energy and / or gas consumption used to flush the system (e.g., constantly provide gas flow to the system to maintain dynamic gas movement within the system). Digital replica 400A may show how energy and / or gas consumption is altered by adjusting one or more gas flow rates (e.g., purge gas) within the processing system.

[0103] In some embodiments, digital replica 400A can utilize process recipe 408A to determine what gases are entering the processing chamber, what reactions are occurring on a substrate disposed in the processing chamber, and what utilization of the gases occurs along with the substrate reaction. Digital replica 400A can further determine what gases and in what amounts remain after the reactions occur on the surface of the substrate. Digital replica 400A can: abatement The digital replica 400A can further determine the amount and type of gas lost through the abatement This can further determine how the final by-products are recycled and the overall impact they have on the environment.

[0104] In some embodiments, one or more substrate processing procedures may require a consistent gas flow to and / or from the processing chamber to process a substrate that meets target process result conditions. The substrate processing system may perform a steady gas flow procedure by implementing one or more flow-to-vent-to-chamber transitions to reduce transient airflow from turning airflow to the chamber on and off. For example, a first gas flow may be initialized and vented, and once the gas flow stabilizes, a steady flow of gas may be provided to the process chamber by directing the vented air to the chamber. The digital replica 400A may determine gas consumption (e.g., gas lost through venting) as a result of this process. For example, the digital replica may identify the transition time and the amount of gas lost through venting during the transient period of initializing or terminating the gas flow. The digital replica may determine an optimization for the transition between venting gas and directing gas to the chamber. Optimizing the transition time may reduce gas lost through venting while identifying the time when the gas reaches a steady state. In some embodiments, the gas flow transition cadence may be determined based on process result requirements, for example, the gas flow transition time may be determined (e.g., optimized) to include a flow rate that does not adversely affect the process result in the corresponding process chamber.

[0105] 4B illustrates a digital replica 400B related to determining eco-efficiency data related to one or more operational states of a physical asset of a manufacturing system. As shown in FIG. 4B, the digital replica 400B may receive data related to one or more operational states of the physical asset of the manufacturing system. For example, the digital replica 400B may receive reduced power data 402B, sleep mode data 404B, shared operational mode data 406B, and process recipe data 408B that indicates one or more processing steps to be performed by the manufacturing system represented by the digital replica 400B.

[0106] Energy savings can occur when one or more physical assets operate in various operating states during operating and idle times. For example, at different steps in a manufacturing process, various elements of a sub-fab facility may not be needed and therefore may be placed in a sleep, idle, hibernate, or off state depending on how soon the elements are likely to be needed. Examples of power-saving low-power states include an idle, sleep, and hibernate state. The primary differences between the three power-saving states are duration and energy consumption. Deeper levels of idle mode energy savings, such as sleep or hibernate, require longer periods of time to recover from an energy-saving mode to achieve full production without impacting the quality or yield of the manufacturing process. Recovery of a process chamber and associated sub-fab facility to best known method (BKM) temperatures and pressures can take seconds, minutes, or hours, depending on the degree of deviation from BKM chamber conditions associated with the power-saving state of the sub-fab facility and process chamber. The idle state typically lasts for a few seconds, the sleep state typically lasts for a few minutes, and the hibernate state typically lasts for a few hours.

[0107] Digital replica 400B may identify one or more operational / power states of the manufacturing system's physical assets and determine the impact of using that power state in a given scenario (e.g., system hardware architecture, subsystem hardware architecture, one or more process strategies of a process, performing some scheduled process, etc.) For example, digital replica 400B may be part of a digital twin that determines such power state and its impact of the scenario for idle or full power or modulation before actually implementing power adjustment(s) on the manufacturing system.

[0108] Process tools and associated manufacturing system sub-fabs may have a variety of different power configurations based on operational needs, for example, various airflow and power configurations to perform shutdown operations after completing manufacturing operations. abatement Power configurations may exist in which a process tool is in an "off" state while the system is operating at full capacity. In this application, the term "low power configuration" refers to any state in which one or more elements of a process tool and / or manufacturing system sub-fab are instructed by one or more controllers to operate in a power conservation mode, such as a different level of energy consumption during a particular process recipe step, or in a non-productive idle mode of operation, such as the idle, sleep, and hibernate states described above, or an off state.

[0109] In some embodiments, one or more support assets may provide support functionality to two or more other physical assets. For example, pumping of two process chambers may be performed by a single pump. Leveraging support assets to alternate operations between two physical assets may reduce energy and overall environmental costs.

[0110] Digital replica 400B may identify environmental resource consumption data 410B associated with one or more physical assets operating in one or more corresponding operational modes. Digital replica 400B may provide recommendations for reducing environmental consumption costs by recommending that one or more physical assets utilize reduced power states, sleep mode states, hibernation states, and / or shared operational mode data during periods when the corresponding tools are idle or experiencing low demand for the physical assets.

[0111] 4C illustrates a digital replica 400C related to determining eco-efficiency data associated with one or more designs of physical assets of a manufacturing system. The digital replica 400C may receive as input any one or more of asset geometry data 402C, asset replacement data 404C, asset configuration data 406C, and process recipes 408C to determine environmental resource consumption data 410C.

[0112] The asset geometry data 402C may indicate one or more sizes, shapes, and dimensions of one or more physical assets of the manufacturing system. For example, a substrate processing chamber may include specifications indicating a first geometric layout. The geometric layout of the processing chamber (e.g., size, dimensions, overall shape, pedestal location, plasma source-to-substrate distance, etc.) may be varied to improve performance. However, varying the geometric layout of the processing chamber may affect environmental resource requirements. For example, evacuating a chamber with a larger volume is generally more resource-intensive than evacuating a chamber with a smaller volume. In another example, cleaning a chamber with tighter crevices may require a larger amount of purge gas. Many of these geometric considerations affect the environmental resource consumption for performing one or more processes of the manufacturing system. The digital replica 400C may receive asset geometry selections and / or asset geometry updates and determine corresponding environmental resource consumption data corresponding to the selections and / or updates.

[0113] The asset replacement data 404C indicates a change (e.g., replacement) of one or more physical assets of the manufacturing system. The asset replacement data 404C may include one or more operating parameters of the old asset and the new asset. For example, an old pump with operational specifications (e.g., pump speed, pump load, energy requirements, etc.) may be replaced with a new pump with new operational specifications. The digital replica 400C may receive data related to the replacement of one or more physical assets and determine an effective update of environmental resource consumption associated with the replacement. In some embodiments, the asset replacement may include other physical assets of the manufacturing system that support and / or are possibly related to the manufacturing system, such as power supplies, heaters, gas flow devices, fluid flow devices, elements of process chambers such as plasma sources, substrate processing equipment, vacuum sealing equipment, metrology equipment, system sensors, processing devices, etc.

[0114] The asset configuration data 406C indicates one or more configurations of one or more assets of the manufacturing system. The asset configuration data 406C indicates how changes in the manufacturing system may affect downstream processes. For example, if a heater is changed, the digital replica 400C may determine the impact on substrate processing (e.g., wafer etching) efficiency, which may require other systems to compensate for the changes in other subsystems of the manufacturing system. In another example, the manufacturing system may be modified by adding insulation and / or heat shields in a processing chamber to reduce heat lost to exhaust and environmental efficiency. The addition of insulation and / or heat shields may affect the processing of substrates in the processing chamber and may result in updated environmental resource costs. The digital replica 400C receives changes to the system and determines environmental resource updates due to the impact on upstream and / or downstream processes and / or procedures.

[0115] As described above, subsystems of a manufacturing system may dynamically consume environmental resources depending on hardware, operating standards, and processing procedures, among other factors. Changes to a subsystem (e.g., asset replacement, operational state change, system configuration update, etc.) similar to those previously described may cause the subsystem to change one or more operations, which may result in updates to associated environmental impacts. One or more changed operations may affect the environmental resource consumption of other coupled subsystems (e.g., increase or decrease environmental resource consumption). For example, multiple RF devices located close to each other may adversely affect performance, and a process may require a greater amount of resources (e.g., energy) to perform a manufacturing process. The digital replica 400C may determine the environmental resource consumption of a second system based on changes to the first system. The digital replica 400C may quantify subsystem coupling (e.g., the impact of another subsystem on the subsystem's environmental efficiency and environmental resource consumption).

[0116] 4D illustrates a digital replica 400D related to determining eco-efficiency data related to performing preventive maintenance (PM) and / or cleaning of physical assets of a manufacturing system. As shown in FIG. 4D, the digital replica 400D may receive purge gas data 402D, cleaning process data 404D, preventive maintenance data 406D, chamber recovery data 407D, and process recipes 408D, as well as environmental resource consumption 410D.

[0117] Substrate processing may include a series of processes to produce electrical circuits in semiconductor, e.g., silicon, wafers according to a circuit design. These processes may be performed in a series of chambers. The successful operation of a modern semiconductor manufacturing facility may aim to facilitate a steady stream of wafers to be moved from one chamber to another in the process of forming electrical circuits in the wafers. In processes that perform many substrate procedures, the conditions of the processing chambers may degrade the processed substrates, causing the processed substrates to fail to meet desired conditions or process results (e.g., critical dimensions, process uniformity, thickness dimensions, etc.).

[0118] The cleaning process data 404D may indicate one or more parameters associated with the cleaning process, such as cleaning duration, frequency, and / or etchant flow. The cleaning process may utilize several environmental resources, such as cleaning materials, precursors, etchants, and / or other substances utilized to perform the cleaning procedure. For example, the cleaning procedure may be performed at a certain cadence or frequency (e.g., after a certain amount of processed wafers) so that the process results of future substrates meet threshold conditions (e.g., process uniformity, critical dimensions, etc.). The frequency of process chamber cleaning may be adjusted (e.g., optimized) to identify a cleaning frequency that results in substrates processed by the chamber still operating under this cleaning frequency schedule to meet threshold conditions (e.g., minimum process result requirements). For example, a multi-wafer cleaning procedure may be performed that conserves environmental resources, such as cleaning materials, precursors, etchants, and / or other substances utilized to perform the cleaning procedure. The digital replica 400D may receive the cleaning data and determine cleaning optimization, such as updating the cleaning duration, frequency, amount of cleaning agent used, etc.

[0119] The preventive maintenance data 406D indicates one or more of the type, frequency, duration, etc. of one or more preventive maintenance procedures associated with one or more physical assets of the manufacturing system. Preventive maintenance procedures (e.g., chamber cleans) are often used as part of a chamber recovery process to return the conditions of a processing chamber to a state suitable for entering a substrate processing production mode (e.g., processing a large volume of substrates). Recovery procedures are often used after preventive maintenance procedures to prepare the chamber (e.g., "warm up" the chamber) for the production mode.

[0120] The chamber recovery data 407D indicates one or more of the type, frequency, duration, etc. of one or more chamber recovery procedures associated with one or more physical assets of the manufacturing system. A common recovery procedure conventionally employed is seasoning the processing chamber. Chamber seasoning is a procedure that involves processing a series of substrates (e.g., blank silicon wafers) to restore chamber conditions (e.g., to coat the chamber walls) that are suitable for production substrate processing (e.g., the substrates processed in the chamber have process results that meet desired threshold criteria). After chamber seasoning, the chamber may operate in a production mode for a period of time until another round of preventative maintenance and further chamber seasoning is required or otherwise recommended to restore the processing chamber conditions.

[0121] The purge gas data 402D may indicate the type, amount, frequency, flow rate, and cleaning duration of the purge gas. The digital replica 400D may determine the impact of changing one or more operating parameters related to the purge gas employed. For example, the digital replica 400D may determine environmental resource consumption updates based on switching to a purge procedure using an alternative purge gas type, such as H2, N2, clean dry air (CDA), etc.

[0122] 4E illustrates a digital replica 400E related to determining eco-efficiency data related to one or more operating conditions of a physical asset of a manufacturing system. As shown in FIG. 4E, the digital replica 400E may include receiving coolant loop configuration data 402E, process cooling water (PCW) data 404E, ambient air data 406E, and / or process recipes 408E to determine environmental resource consumption data 410E.

[0123] Process chambers utilized in substrate processing typically include several internal components that are repeatedly heated and cooled during and after a process is performed. In some cases, for example, when routine service or maintenance is required after a process is performed in the process chamber, the components are cooled to approximately room temperature. For example, in a temperature-controlled component, such as a process chamber showerhead having coolant channels, to cool the component from a typical operating temperature (e.g., about 90 degrees Celsius), the heat source heating the component can be shut off and coolant is flowed through the coolant channels to extract heat from the component.

[0124] The coolant loop configuration data 402E indicates one or more geometries of one or more coolant loops configured to extract heat from one or more physical assets of the manufacturing system. One or more coolant loops may operate in parallel, causing multiple loops to cool a common area of ​​a physical asset. One or more coolant loops may cool multiple physical assets in series with each other. The process cooling water (PCW) data 404E indicates one or more parameters of the coolant material, such as the type, flow rate, and temperature of the coolant (e.g., process cooling water (PCW)). The digital replica may include a heat flow model that indicates where energy is transferred within the environment of the manufacturing system utilizing one or more coolant loops. The digital replica 400E may identify modifications of physical assets (e.g., chambers, chamber walls, chamber systems) that direct heat to the cooling loops and the associated environmental efficiencies saved by directing heat to the cooling loops. The digital replica 400E may further determine the impact on process results when PCW modulation occurs. PCW modulation may involve varying the flow rate within a cooling loop to vary the heat exchange within a physical asset of a manufacturing system.

[0125] In some embodiments, digital replica 400E can be part of a digital twin that analyzes and determines eco-efficiency associated with process cooling loop configurations. For example, modifications to a cooling loop can include adjustments to size, flow rate, geometry, and looping configuration. Digital replica 400E can determine updates to eco-efficiency data based on adjustments to coolant flow from parallel to serial loops. For example, digital replica 400E can determine the overall consumption and carbon footprint of the system based on various cooling loop configurations and / or updates to various cooling loop configurations.

[0126] The ambient air data 408E indicates heat transfer escaping through the ambient air. The ambient air data 408E can be utilized to determine heat loss and / or energy loss through the ambient air. The ambient air data 408E can also indicate heat loss external to the manufacturing system, but in the environment supporting the manufacturing system. For example, the ambient air data 408E can indicate heat flow within the environment surrounding the manufacturing system, such as a laboratory system, where the heat flow is indicated by the heating, ventilation, and air conditioning (HVAC) unit flow rate and process load supporting the environment containing the manufacturing system. In some embodiments, the digital replica 400E can determine the energy applied to the chamber to perform a process therein and the energy lost through radiation through the chamber walls.

[0127] FIG. 5 is an exemplary illustration of operational parameter constraints 500 for manufacturing process steps according to some implementations of the present disclosure. Various manufacturing process steps may include operational parameter constraints 500 that indicate process parameter windows 510 or sets of values ​​(e.g., combinations of values) for corresponding sets of parameters that, when satisfied, result in satisfying a threshold condition (e.g., a minimum quality condition). For example, the process parameter window 510 may include a first parameter 502 (e.g., a first flow rate of a first gas) and a second parameter 504 (e.g., a temperature of the gas). To perform a manufacturing process and meet a threshold condition (e.g., a minimum quality specification, a statistical process control (SPC) limit, a specification limit, etc.), the process parameter value window 510 is determined, identifying a parameter value combination that will result in a product that is likely to meet the threshold condition. As shown in FIG. 5, the process parameter window 510 includes a lower limit 506A and an upper limit 506A for the first parameter 502 and a lower limit 508B and an upper limit 508A for the second parameter.

[0128] The optimizations identified by the manufacturing process system (e.g., using the adaptive optimization algorithm 216 and / or the physics-based model 214) may include determining environmentally optimized process parameter windows 512 within the process parameter window 510 that cause the manufacturing operation to consume a reduced amount of resources compared to process parameter values ​​outside the environmentally optimized process parameter window 512.

[0129] 5 shows a simplified process parameter window 510 and an environmentally optimized process parameter window 512 that depend on only two parameters 502, 504. Both the process parameter window 510 and the environmentally optimized process parameter window 512 form a simple rectangle. The process parameter windows may include more than two parameters and may include more diverse parameter dependencies. For example, nonlinear, physics-based, statistical, and / or empirical relationships between parameters may result in nonlinear and environmentally optimized process parameter windows.

[0130] 6-8 illustrate flow diagrams illustrating exemplary methods 600-800 related to training and / or using machine learning models in relation to environmental resource consumption and / or environmental impact of manufacturing processes, according to some implementations of the present disclosure. For simplicity of explanation, methods 600, 700A-C are shown and described as a series of acts. However, acts in accordance with the present disclosure may occur in various orders and / or simultaneously, as well as with other acts not presented and described herein. Furthermore, not all of the acts shown may be performed to implement methods 600-800 in accordance with the disclosed subject matter. Additionally, those skilled in the art will understand and appreciate that methods 600-800 may alternatively be represented as a series of interrelated states via state diagrams or events.

[0131] FIG. 6 is an illustration of a method for exploring modifications to a manufacturing process and the resulting impact on eco-efficiency, according to some implementations of the present disclosure.

[0132] At block 601, processing logic receives first data indicative of an update to a substrate manufacturing system that includes a first configuration of manufacturing equipment and operates according to one or more process procedures.

[0133] At block 602, processing logic determines environmental resource usage data using the first data in conjunction with the digital replica. The digital replica includes a digital replica of the substrate manufacturing system. The environmental resource usage data indicates environmental resource consumption corresponding to performing one or more process steps by the substrate manufacturing system incorporating the updates.

[0134] In some embodiments, the update includes replacing a first hardware subsystem device with a second hardware subsystem device having one or more operating specifications different from the first hardware subsystem device. In an exemplary embodiment, the update includes changing a first configuration of the manufacturing facility to a second configuration of the manufacturing facility. In an exemplary embodiment, the update includes changing a scheduled operating mode of a physical asset of the substrate manufacturing system, the scheduled operating mode including a reduced power mode. In an exemplary embodiment, the update includes changing a scheduled operating mode of a support asset of the substrate manufacturing system to a shared operating mode, the support asset operating in the shared operating mode alternating between performing support functions for multiple physical assets of the substrate manufacturing system that perform one or more process steps.

[0135] In some embodiments, the digital replica includes a physics-based model of one or more physical assets of the substrate manufacturing system. The physics-based model may indicate a relationship between the size and geometry of a substrate processing chamber and environmental resource consumption. The update may relate to a modification to at least one of the size or geometry of the substrate processing chamber. The physics-based model may indicate a relationship between the type of purge gas used in the substrate manufacturing system and environmental resource consumption. The update may relate to how environmental resource consumption is changed by modifying the type and amount of gas used to purge the system. For example, a particular resource consumption update may include how environmental resource consumption is affected by changing the purge gas used from a first purge gas, such as nitrogen, to clean dry air (CDA). The physics-based model may indicate a relationship between at least one of the heat extraction procedures from the substrate manufacturing system and environmental resource consumption. The update may relate to a modification to a heat rejection device, a gas abatement The method may involve modifications to at least one of the device, the water cooling device, or the ventilation structure.

[0136] At block 603, processing logic provides environmental resource usage data for display on a graphical user interface (GUI).

[0137] 7A-7C are flow diagrams of methods 700A-C related to identifying modifications to a manufacturing process according to some implementations of the present disclosure. Methods 700A-C may be performed by processing logic, which may include hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, a processing device, etc.), software (e.g., instructions executing on a processing device, a general-purpose computer system, or a dedicated machine), firmware, microcode, or a combination thereof. In some embodiments, method 700A may be performed in part by machine learning system 170 (e.g., server machine 172, dataset generator 174, etc.). Machine learning system 170 may use method 700A to at least one of train, validate, or test a machine learning model according to embodiments of the present disclosure. In some embodiments, one or more operations of method 700A may be performed by dataset generator 174 of server machine 172. In some embodiments, methods 700B-C may be performed in part by machine learning system 170 (e.g., server machine 172, server machine 180, server machine 192, etc.). Machine learning system 170 may use method 700B to train a machine learning model according to embodiments of the present disclosure. Machine learning system 170 may use method 700C to use a trained machine learning model according to embodiments of the present disclosure. In some embodiments, one or more operations of methods 700B-C may be performed by correction identification component 194 of machine learning system 170. Note that components described with respect to one or more of FIGS. 1-6 may be used to illustrate aspects of FIGS. 7A-7C. In some embodiments, a non-transitory storage medium stores instructions that, when executed by a processing device (e.g., of machine learning system 170), cause the processing device to perform methods 700A-C.

[0138] For simplicity of explanation, methods 700A-C are shown and described as a series of acts. However, acts in accordance with the present disclosure may occur in various orders, simultaneously, in parallel with multiple instances per store, and / or with other acts not shown and described herein. Moreover, not all of the acts shown may be performed to implement methods 700A-C in accordance with the disclosed subject matter. Additionally, those skilled in the art will understand and appreciate that methods 700A-C may alternatively be represented as a series of interrelated states via a state diagram or events.

[0139] Referring to FIG. 7A, a method 700A relates to generating a dataset for a machine learning model to process a selection of manufacturing processes and / or manufacturing equipment to identify modifications to inputs.

[0140] At block 702, processing logic implementing method 700A initializes a training set T to an empty set.

[0141] At block 704, processing logic generates a first data input (e.g., a first training input, a first validation input) that includes a selection of a manufacturing process and manufacturing equipment.

[0142] In some embodiments, at block 706, processing logic generates a first target output for one or more of the data inputs (e.g., the first data input). The first target output may be, for example, a modification to the manufacturing process and / or manufacturing equipment. Processing logic may generate the target output based on an input selection of the manufacturing process and / or manufacturing equipment.

[0143] At block 708, processing logic optionally generates mapping data indicating an input / output mapping. The input / output mapping (or mapping data) may refer to data inputs (e.g., one or more of the data inputs described herein), target outputs for the data inputs (e.g., the target outputs identify output data), and associations between the data input(s) and the target outputs. The processing logic may, for example, perform gradient descent and backpropagation to update weights for nodes in one or more layers of the machine learning model.

[0144] At block 710, processing logic adds the data input generated at block 704 and / or the mapping data generated at block 708 to the dataset T.

[0145] At block 712, processing logic branches based on whether dataset T is sufficient for at least one of training, validating, and / or testing machine learning model 190. If so, execution proceeds to block 714; otherwise, execution continues back to block 704. In some embodiments, the sufficiency of dataset T may be determined simply based on the number of input / output mappings in the dataset, while in some other implementations, the sufficiency of dataset T may be determined based on one or more other criteria (e.g., diversity of data examples, a measure of accuracy, etc.) in addition to or instead of the number of input / output mappings.

[0146] At block 714, the processing logic provides dataset T (e.g., to server machine 180) for training, validating, and / or testing machine learning model 190. In some embodiments, dataset T is a training set and is provided to training engine 182 of server machine 180 to perform training. In some embodiments, dataset T is a validation set and is provided to validation engine 184 of server machine 180 to perform validation. In some embodiments, dataset T is a test set and is provided to test engine 186 of server machine 180 to perform testing. In the case of a neural network, for example, input values ​​(e.g., numerical values ​​associated with data inputs) of a given input / output mapping are input to the neural network, and output values ​​(e.g., numerical values ​​associated with target outputs) of the input / output mapping are stored in output nodes of the neural network. Connection weights in the neural network are then adjusted according to a learning algorithm (e.g., backpropagation, etc.), and this procedure is repeated for other input / output mappings in dataset T. After block 714, the machine learning model (e.g., machine learning model 190) may be at least one of trained using training engine 182 of server machine 180, validated using validation engine 184 of server machine 180, or tested using testing engine 186 of server machine 180. The trained machine learning model may be implemented by correction identification component 194 (of machine learning system 170) to generate output data for further use by server 120 procedures (e.g., component synthesis tool 122, digital replica tool 124, optimization tool 126, strategy builder tool 128, and / or resource consumption tool 130).

[0147] Referring to FIG. 7B, method 700B relates to training a machine learning model to determine modifications to manufacturing processes and / or manufacturing equipment that improve eco-efficiency.

[0148] At block 720, processing logic receives a selection of a manufacturing process or manufacturing facility.

[0149] In some embodiments, processing logic identifies a label corresponding to a modification to a manufacturing process at block 722. In some embodiments, the label indicates the modification to the manufacturing equipment part and / or manufacturing process and an associated reduction in environmental resource consumption and / or environmental impact.

[0150] At block 724, processing logic trains a machine learning model using data inputs including manufacturing process data (e.g., and target outputs including labels) to generate a trained machine learning model configured to generate outputs (e.g., modifications) that can be applied to the manufacturing process to reduce environmental resource consumption and / or environmental impact.

[0151] In some embodiments, the machine learning model is trained using unsupervised learning based on the data input (e.g., without a target output) to generate the trained machine learning model (e.g., to cluster the data). In some embodiments, the machine learning model is trained using supervised learning based on the data input and the target output to generate the trained machine learning model.

[0152] Referring to FIG. 7C, method 700C relates to using machine learning models to determine modifications to manufacturing processes and / or manufacturing equipment to improve (e.g., optimize) eco-efficiency (e.g., reduce environmental resource consumption and / or environmental impact).

[0153] At block 740, processing logic receives current manufacturing process data. At block 742, processing logic provides the current data (e.g., manufacturing process data) to a trained machine learning model. The trained machine learning model may be trained by method 700B.

[0154] At block 744, processing logic obtains one or more outputs from the trained machine learning model. In some embodiments, the outputs include modifications to the manufacturing process and / or manufacturing equipment that, when implemented, improve the environmental efficiency of the manufacturing process and / or manufacturing equipment. At block 746, processing logic causes the application of one or more manufacturing process modifications to the manufacturing process based on the output(s).

[0155] 8 is an example illustration of a method 800 for identifying modifications to a manufacturing process according to some implementations of the present disclosure. Method 800 is performed by processing logic, which may include hardware (circuitry, dedicated logic, etc.), software (such as running on a general-purpose computer system or a dedicated machine), or any combination thereof. In one implementation, the method is performed using server 120 and trained machine learning model 190 of FIG. 1 , although in some other implementations, one or more blocks of FIG. 6 may be performed by one or more other machines not shown in the figure.

[0156] The method 800 may include receiving a selection of at least one of a first manufacturing process or a first manufacturing facility for performing manufacturing operations of the first manufacturing process, and identifying modifications and / or optimizations to improve eco-efficiency (reducing resource consumption and / or environmental impact). The manufacturing process may be associated with a manufacturing system (e.g., manufacturing system 102 of FIG. 1 ).

[0157] At block 801, processing logic receives a first selection of at least one of a first manufacturing process or a first manufacturing equipment (e.g., tool 202 and subcomponent 204 of FIG. 2 ) for performing manufacturing operations of the first manufacturing process.

[0158] At block 802, processing logic inputs the first selection into a digital replica of the first manufacturing facility (e.g., digital replicas 400A-E of FIGS. 4A-4E). In some embodiments, the digital replica may include a physics-based model (e.g., physics-based model 214 of FIG. 2).

[0159] At block 803, processing logic determines environmental resource usage data indicative of a first environmental resource consumption and / or environmental impact of a first manufacturing process performed on the first manufacturing equipment based on physical conditions of the first manufacturing process. In some embodiments, the environmental resource usage data includes at least one of energy consumption, gas consumption, or water consumption associated with the first manufacturing equipment for performing manufacturing operations of the first manufacturing process.

[0160] In some embodiments, the processing logic uses the environmental resource usage data as input to a machine learning model. The processing logic further includes obtaining one or more outputs of the machine learning model, the one or more outputs indicating a modification. In some embodiments, the one or more outputs of the machine learning model may further indicate a level of confidence that the modification, when implemented, will reduce the first environmental resource consumption and / or environmental impact of the manufacturing process. The processing logic further determines that the level of confidence for the modification satisfies a threshold condition.

[0161] In some embodiments, the first environmental resource consumption and / or environmental impact includes environmental resource consumption and / or environmental impact associated with one of a replacement procedure or a maintenance procedure for a consumable part of the first manufacturing facility.

[0162] In some embodiments, the environmental resource usage data includes life cycle data for components associated with the manufacturing facility. The remediation may further include implementing corrective actions associated with the components.

[0163] At block 804, processing logic determines modifications to the first manufacturing process that reduce environmental resource consumption and / or environmental impact (e.g., resource consumption per unit) of the first manufacturing process performed on the first manufacturing facility. In some embodiments, the modifications include altering one or more of the manufacturing process steps and / or manufacturing facility processing parameters. In some embodiments, prioritized optimization of one or more of energy consumption, gas consumption, or water consumption of the first manufacturing facility for performing manufacturing operations of the first manufacturing process.

[0164] At block 805, processing logic optionally applies the corrections to the manufacturing process. At block 806, processing logic optionally provides the corrections for display by a graphical user interface (GUI). In some embodiments, multiple corrections are determined and provided for presentation on the graphical user interface (GUI) (e.g., for presentation to a user, such as a system operator). In some embodiments, the corrections are presented to the user in rank order by level of confidence. In some embodiments, the corrections are presented to the user with a visual indicator representing the confidence level associated with each normative behavior. For example, one or more corrections with the highest confidence level may be indicated in a first color (e.g., green or gold), and one or more corrections with a confidence level close to a threshold level may be indicated in a second color (e.g., yellow or silver). In some embodiments, the corrections may be placed in a hierarchy or group based on their associated confidence levels.

[0165] In some embodiments, processing logic may further determine, based on the environmental resource usage data, that the first selection fails to satisfy an eco-efficiency threshold. Processing logic may further perform optimization of the first selection in response to determining that the first selection fails to satisfy the eco-efficiency threshold. Performing optimization of the first selection includes identifying one or more modifications to the first selection that, when applied, result in an updated eco-efficiency that satisfies the eco-efficiency threshold.

[0166] In some embodiments, the processing logic will further receive a second selection of manufacturing equipment for performing a second manufacturing operation in the second manufacturing process. The processing may receive second sensor data associated with the second manufacturing operation from one or more sensors associated with the second selection of manufacturing equipment. The processing logic may further update the digital replica to generate an updated digital replica, the updated digital replica associated with the first and second selections of manufacturing equipment. The processing logic may further obtain one or more outputs from the digital replica including aggregated environmental resource data indicative of the environmental resource consumption and / or environmental impact (e.g., consumption and / or impact per unit) of the first and second selections.

[0167] 9A-9C show exemplary environmental resource consumption reports according to some embodiments. Figure 9A shows an environmental resource consumption report 900A showing contributions by first sub-level objects, such as components of a process chamber. For example, one or more of the contributing components found in the report may include power consumption by one or more of the source RF, turbo pump, wall HX, cathode chiller, DC power, ESC heater, foreline heater, gas panel, lid heater, and roughing pump, among others.

[0168] 9B shows an environmental resource consumption report 900B corresponding to a level tool analysis. For example, a process tool may include various components, such as various process chambers. The individual contributions of the subcomponents may be aggregated from report 900A as entries or categories into report 900B.

[0169] 9C shows an environmental resource consumption report 900C corresponding to a fleet-level analysis. Each process tool in the fleet can have an identified and corresponding contribution represented in the cart. For example, the tool-level contributions shown in report 900B can be aggregated and used as entries or categories in report 900C.

[0170] 10A-10E show various views of an integrated environmental resource consumption dashboard graphical user interface (GUI) 1000A-E, according to some embodiments.

[0171] FIG. 10A illustrates an integrated environmental resource consumption dashboard GUI 1000A, a tool-level view of environmental resource consumption data. As shown in FIG. 10A, GUI 100A includes a system diagram 1002. The system diagram may include a schematic or mapping of one or more process tools. System diagram 1002 may include identifying information for another process tool in the system. GUI 1000A includes an electricity consumption breakdown 1004 and a gas consumption breakdown 1006. Electricity consumption breakdown 1004 shows one or more electricity consumption contributions of subcomponents of an identified system (e.g., system diagram 1002). Subcomponents may include items such as pumps, power sources, HVAC, gas flows, heaters, coolers, etc.

[0172] Gas consumption breakdown 1006 is similar to electricity consumption breakdown 1004 and provides gas consumption or losses attributable to one or more identified sub-components of the system. GUI 1000A may include a time-distributed representation of the system's electricity consumption and / or the system's gas consumption. For example, a 7-day electricity consumption history 1008 may be shown by GUI 1000A. As shown in FIG. 10A, GUI 1000A abatement Contains data 1010. abatement Data 1010 is from the system abatement Provide a breakdown of the content and / or quality of one or more of the nuclides detected.

[0173] As shown in FIG. 10A, GUI 1000A may include a navigation pane 1010 (eg, interactable buttons) that directs the user to different views, such as those presented by GUIs 1000B-E.

[0174] FIG. 10B shows an overall environmental resource consumption dashboard GUI 1000B, a daily cumulative view of environmental resource consumption data. As shown in FIG. 10B, GUI 1000B includes a daily electricity consumption breakdown 1020. The daily electricity consumption breakdown may show the electricity used for the given data and various statistical measures related to daily electricity consumption. The daily electricity consumption may break down the total daily electricity consumption into more granular data, such as by showing a graphic showing the breakdown of electricity consumption attributable to various devices in the system.

[0175] As shown in FIG. 10B , GUI 1000B includes a daily gas consumption breakdown 1022. The daily gas consumption breakdown may show the gas used or lost for a given date and various statistical measures related to daily gas consumption. The daily gas consumption may break down the total gas consumption per day into more granular data, such as by showing a graphic showing the breakdown of gas consumption attributable to various devices in the system. GUI 1000B may include a time-distributed representation of the system's electricity consumption and / or the system's gas consumption. For example, a 7-day electricity consumption history 1024 may be shown by GUI 1000B. As shown in FIG. 10B , GUI 1000B may include a navigation pane 1026 (e.g., interactable buttons) that allow a user to access different views of the GUI (e.g., with different representations of eco-efficiency data).

[0176] FIG. 10C illustrates an aggregate environmental resource consumption dashboard GUI 1000C, a strategy view of environmental resource consumption data. As shown in FIG. 10C, GUI 1000C may illustrate electricity consumption per strategy 1040. Electricity consumption per strategy 1040 may indicate the electricity cost attributable to executing one or more identified process strategies. The electricity consumption per strategy may further indicate one or more quantities indicating the number of times the corresponding strategy is implemented. As shown in FIG. 10C, GUI 1000C may illustrate gas consumption per strategy 1040. Gas consumption per strategy 1040 may indicate the gas usage or gas loss attributable to executing one or more identified process strategies. The electricity consumption per strategy may further indicate one or more quantities indicating the number of times the corresponding strategy is implemented. As shown in FIG. 10C, GUI 1000C may include a navigation pane 1044 (e.g., interactable buttons) that enable a user to access different views of the GUI (e.g., with different representations of eco-efficiency data).

[0177] FIG. 10D illustrates an overall environmental resource consumption dashboard GUI 1000D, a per-wafer view of environmental resource consumption data. As shown in FIG. 10D, GUI 1000D may show electricity consumption per wafer 1060. Electricity consumption per wafer 1060 may indicate the electricity cost attributable to performing a process procedure for an individual wafer. As shown in FIG. 10D, GUI 1000D may show gas consumption per wafer 1062. Gas consumption per wafer 1062 may indicate the electricity cost attributable to performing a process procedure for an individual wafer. GUI 1000D may include a time-distributed representation of the system's electricity consumption per wafer and / or the system's gas consumption per wafer. For example, a 7-day electricity consumption history 1064 may be shown by GUI 1000D. As shown in FIG. 10D, GUI 1000D may include a navigation pane 1066 (e.g., interactable buttons) that enable a user to access different views of the GUI (e.g., with different representations of eco-efficiency data).

[0178] FIG. 10E illustrates an aggregate environmental resource consumption dashboard GUI 1000E, a fleet-level view of environmental resource consumption data. As shown in FIG. 10E, GUI 1000E illustrates fleet data 1080. Fleet data 1080 illustrates system identification (ID), location ID, and time data. Fleet data 1080 may include a list of tools in an identified fleet. As shown in FIG. 10E, GUI 1000E includes an electricity consumption breakdown 1082 of the tools in the fleet. GUI 1000E may include a gas consumption breakdown 1084 among the tools in the fleet. GUI 1000E may include a time-distributed representation of the electricity consumption of the fleet of tools and / or the consumption of the fleet of tools. For example, a 7-day electricity consumption history 1086 may be shown by GUI 1000E. As shown in FIG. 10E, the GUI 1000E may include a navigation pane 1088 (e.g., interactable buttons) that allow the user to access different views of the GUI (e.g., with different representations of eco-efficiency data).

[0179] 11 illustrates a block diagram of an exemplary computing device 1100 that may operate in accordance with one or more aspects of the present disclosure. In various illustrative examples, various components of computing device 1100 may represent various components of client device 150, server 120, data store 112, and machine learning system 170 shown in FIG.

[0180] The exemplary computing device 1100 may be connected to other computer devices in a LAN, an intranet, an extranet, and / or the Internet (e.g., using a cloud environment, cloud technology, and / or edge computing). The computing device 1100 may operate in the capacity of a server in a client-server network environment. The computing device 1100 may be a personal computer (PC), a set-top box (STB), a server, a network router, switch, or bridge, or any device capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by the device. Furthermore, although only a single exemplary computing device is shown, the term “computer” shall also be taken to include any collection of computers that individually or together execute a set (or sets) of instructions to perform any one or more of the methodologies described herein.

[0181] The exemplary computing device 1100 may include a processing device 1102 (also referred to as a processor or CPU), a main memory 1104 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM), etc.), a static memory 1106 (e.g., flash memory, static random access memory (SRAM), etc.), and a secondary memory (e.g., a data storage device 1118), which may communicate with each other via a bus 1130.

[0182] The processing device 1102 represents one or more general-purpose processing devices, such as a microprocessor, a central processing unit, or the like. More specifically, the processing device 1102 may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a processor implementing other instruction sets, or a processor implementing a combination of instruction sets. The processing device 1102 may also be one or more special-purpose processing devices, such as an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), a network processor, or the like. According to one or more aspects of the present disclosure, the processing device 1102 may be configured to execute instructions implementing the methods 600-800 shown in FIGS. 6-8.

[0183] The exemplary computing device 1100 may further include a network interface device 1108 that may be communicatively coupled to a network 1120. The exemplary computing device 1100 may further include a video display 1110 (e.g., a liquid crystal display (LCD), a touch screen, or a cathode ray tube (CRT)), an alphanumeric input device 1112 (e.g., a keyboard), a cursor control device 1114 (e.g., a mouse), and an acoustic signal generating device 1116 (e.g., a speaker).

[0184] The data storage device 1118 may include a machine-readable storage medium (or, more specifically, a non-transitory machine-readable storage medium) 1128 having stored thereon one or more sets of executable instructions 1122. For example, the data storage may be on-premise or remote physical storage, such as a cloud storage environment. According to one or more aspects of the present disclosure, the executable instructions 1122 may include executable instructions related to performing the methods 600-800 shown in FIGS. 6-8.

[0185] The executable instructions 1122 may also reside, completely or at least partially, within the main memory 1104 and / or within the processing device 1102 during execution of the executable instructions 1122 by the exemplary computing device 1100, with the main memory 1104 and the processing device 1102 also constituting computer-readable storage media. The executable instructions 1122 may further be transmitted or received over a network via the network interface device 1108.

[0186] Although computer-readable storage medium 1128 is shown in FIG. 11 as a single medium, the term "computer-readable storage medium" should be interpreted to include a single medium or multiple media (e.g., centralized or distributed databases, and / or associated caches and servers) that store one or more sets of operating instructions. The term "computer-readable storage medium" should also be interpreted to include any medium that is capable of storing or encoding a set of instructions for execution by a machine that cause the machine to perform any one or more of the methodologies described herein. The term "computer-readable storage medium" should therefore be interpreted to include, but is not limited to, solid-state memory and optical and magnetic media.

[0187] Some portions of the above detailed descriptions have been presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps leading to a desired result. The steps require physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of widespread usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.

[0188] It should be borne in mind, however, that these and similar terms all relate to the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless otherwise indicated, as will be apparent from the following description, it will be appreciated that throughout the description, descriptions utilizing terms such as "identifying," "determining," "storing," "adjusting," "causing," "returning," "comparing," "creating," "stopping," "loading," "copying," "throwing," "exchanging," "performing," and the like refer to the acts and processes of a computer system or similar electronic computing device that manipulate data represented as physical (electronic) quantities in the computer system's registers and memory, and transform that data into other data that are similarly represented as physical quantities in the computer system's memory or registers, or other such information storage, transmission, or display device.

[0189] Examples of the present disclosure also relate to apparatus for performing the methods described herein. This apparatus may be specially constructed for the required purposes, or it may be a general-purpose computer system that is selectively programmed by a computer program stored in the computer system. Such computer programs may be stored on a computer-readable storage medium, such as any type of disk, including, but not limited to, optical disks, compact disk read-only memories (CD-ROMs), and magneto-optical disks, read-only memory (ROM), random access memory (RAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disk storage media, optical storage media, flash memory devices, other types of machine-accessible storage media, or any type of medium suitable for storing electronic instructions, each coupled to a computer system bus.

[0190] The methods and displays presented herein are not inherently related to any particular computer or other apparatus. Various general-purpose systems can be used with programs in accordance with the teachings herein, or it may prove convenient to construct more specialized apparatus to perform the required method steps. The required structure for a variety of these systems appears to be set forth in the description that follows. Additionally, the scope of the present disclosure is not limited to any particular programming language. It will be appreciated that various programming languages ​​can be used to implement the teachings of the present disclosure.

[0191] It should be understood that the above description is illustrative, and not restrictive. Many other implementations will be apparent to those skilled in the art upon reading and understanding the above description. While the present disclosure describes particular examples, it will be recognized that the systems and methods of the present disclosure are not limited to the examples described herein, but may be practiced with modification within the scope of the appended claims. Accordingly, the specification and drawings should be considered in an illustrative, and not a restrictive, sense. The scope of the present disclosure should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

Claims

1. receiving, by a processing device, first data indicative of an update to a substrate manufacturing system including a first configuration of manufacturing equipment and operating according to one or more process procedures, the update comprising: (i) changing a scheduled first operational mode of support assets of the substrate manufacturing system to a shared operational mode, wherein the support assets operating in the shared operational mode alternate performing support functions for multiple physical assets of the substrate manufacturing system that perform the one or more process steps; (ii) modifying a first gas used in the substrate manufacturing system to a second gas; (iii) making modifications to at least one of the size or geometry of a processing chamber of the substrate manufacturing system; or (iv) modifying the flow of one or more gases into a processing chamber of said substrate manufacturing system; receiving first data, the first data including at least one of: determining, by the processing device, environmental resource usage data using the first data in conjunction with a digital replica, the digital replica including a digital copy of the substrate manufacturing system, the environmental resource usage data indicative of environmental resource consumption corresponding to performing the one or more process steps with the substrate manufacturing system incorporating the update; providing, by the processing device, the environmental resource usage data for display on a graphical user interface (GUI); A method comprising:

2. 10. The method of claim 1, wherein the updating further comprises replacing a first hardware subsystem device with a second hardware subsystem device having one or more operating specifications different from the first hardware subsystem device.

3. The method of claim 1 , wherein the updating further comprises changing the first configuration of a manufacturing facility to a second configuration of a manufacturing facility.

4. 10. The method of claim 1, wherein the updating comprises changing a scheduled second operating mode of a physical asset of the substrate manufacturing system, the scheduled second operating mode further comprising a reduced power mode or an idle state.

5. The method of claim 1 , wherein the digital replica comprises a physics-based model of one or more physical assets of the substrate manufacturing system.

6. The method of claim 5 , wherein the physics-based model describes a relationship between the size and geometry of the processing chamber and the environmental resource consumption.

7. the physics-based model indicates a relationship between the types of gases used within the substrate manufacturing system and the environmental resource consumption; The method of claim 5 , wherein the first gas and the second gas comprise at least one of a purge gas, a reactive gas, or an etchant gas.

8. 6. The method of claim 5, wherein the physics-based model indicates a relationship between heat extraction procedures from the substrate manufacturing system and the environmental resource consumption, and wherein the update further includes modifications to at least one of a heat rejection device, a gas abatement device, a water cooling device, or a ventilation structure.

9. determining the environmental resource usage data, Calculating environmental resource consumption metrics for a plurality of tools for manufacturing equipment selection; aggregating the environmental resource consumption metrics for the plurality of tools to determine the environmental resource usage data; The method of claim 1 , comprising:

10. using the digital replica to determine second environmental resource usage data indicative of environmental resource consumption corresponding to performing the one or more process steps; determining a value indicative of an eco-efficiency impact of the update based on a comparison between the environmental resource usage data and the second environmental resource usage data; and The method of claim 1 further comprising:

11. the substrate manufacturing system comprises a process chamber, and the physics-based model comprises: one or more gases entering the processing chamber; one or more reactions occurring on a substrate disposed within the processing chamber; and one or more relationships between the one or more gases and the one or more reactions occurring on the substrate; indicates, the environmental resource consumption indicating one or more amounts of the one or more gases lost through abatement; The method of claim 5.

12. The method of claim 1 , wherein the environmental resource usage data comprises at least one of energy consumption, gas consumption, or water consumption associated with the substrate manufacturing system performing the one or more process steps.

13. Receiving, by a processing device, first data indicative of an update to a substrate manufacturing system including a first configuration of manufacturing equipment and operating in accordance with one or more process procedures; determining, by the processing device, environmental resource usage data using the first data in conjunction with a digital replica, the digital replica including a digital copy of the substrate manufacturing system, the environmental resource usage data indicative of environmental resource consumption corresponding to performing the one or more process steps with the substrate manufacturing system incorporating the update; providing, by the processing device, the environmental resource usage data for display on a graphical user interface (GUI); Using the environmental resource usage data to determine carbon footprint data associated with the substrate manufacturing system incorporating the updates. A method comprising:

14. Memory and a processing device coupled to the memory, the processing device: receiving first data indicative of an update to a substrate manufacturing system including a first configuration of manufacturing equipment and operating according to one or more process procedures, the update comprising: (i) changing a scheduled first operational mode of support assets of the substrate manufacturing system to a shared operational mode, wherein the support assets operating in the shared operational mode alternate performing support functions for multiple physical assets of the substrate manufacturing system that perform the one or more process steps; (ii) modifying a first gas used in the substrate manufacturing system to a second gas; (iii) making modifications to at least one of the size or geometry of a processing chamber of the substrate manufacturing system; or (iv) modifying the flow of one or more gases into a processing chamber of said substrate manufacturing system; receiving first data, the first data including at least one of: determining environmental resource usage data using the first data in conjunction with a digital replica, the digital replica including a digital replica of the substrate manufacturing system, the environmental resource usage data indicative of environmental resource consumption corresponding to performing the one or more process steps with the substrate manufacturing system incorporating the update; providing said environmental resource usage data for display on a graphical user interface (GUI); and The system.

15. the digital replica includes a physics-based model that describes the relationship between the types of gases used within the substrate manufacturing system and the environmental resource consumption; 15. The system of claim 14, wherein the first gas and the second gas comprise at least one of a purge gas, a reactive gas, or an etchant gas.

16. 15. The system of claim 14, wherein the digital replica includes a physics-based model illustrating a relationship between at least one of heat extraction procedures from the substrate manufacturing system and the environmental resource consumption, and wherein the update further includes modifications to at least one of a heat rejection device, a gas abatement device, a water cooling device, or a ventilation structure.

17. 15. The system of claim 14, wherein the environmental resource usage data comprises at least one of energy consumption, gas consumption, water consumption, or carbon footprint indicators associated with the substrate manufacturing system performing the one or more process steps.

18. A non-transitory machine-readable storage medium comprising instructions that, when executed by a processing device, cause the processing device to: receiving first data indicative of an update to a substrate manufacturing system including a first configuration of manufacturing equipment and operating according to one or more process procedures, the update comprising: (i) changing a scheduled first operational mode of support assets of the substrate manufacturing system to a shared operational mode, wherein the support assets operating in the shared operational mode alternate performing support functions for multiple physical assets of the substrate manufacturing system that perform the one or more process steps; (ii) modifying a first gas used in the substrate manufacturing system to a second gas; (iii) making modifications to at least one of the size or geometry of a processing chamber of the substrate manufacturing system; or (iv) modifying the flow of one or more gases into a processing chamber of said substrate manufacturing system; receiving first data, the first data including at least one of: determining environmental resource usage data using the first data in conjunction with a digital replica, the digital replica including a digital replica of the substrate manufacturing system, the environmental resource usage data indicative of environmental resource consumption corresponding to performing the one or more process steps with the substrate manufacturing system incorporating the update; providing said environmental resource usage data for display on a graphical user interface (GUI); and A non-transitory machine-readable storage medium that causes

19. 20. The non-transitory machine-readable storage medium of claim 18, wherein the environmental resource usage data comprises at least one of energy consumption, gas consumption, water consumption, or carbon footprint indicators associated with the substrate manufacturing system performing the one or more process steps.

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